restructured sources

108 files changed, 18785 insertions, 0 deletions

diff --git a/build/FindAnacondaEnvironment.cmake b/build/FindAnacondaEnvironment.cmake
new file mode 100644
index 0000000..6475128
--- /dev/null
+++ b/build/FindAnacondaEnvironment.cmake
@@ -0,0 +1,154 @@
+#   Copyright 2017 Edoardo Pasca
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+
+# #.rst:
+# FindAnacondaEnvironment
+# --------------
+#
+# Find Python executable and library for a specific Anaconda environment
+#
+# This module finds the Python interpreter for a specific Anaconda enviroment, 
+# if installed and determines where the include files and libraries are.  
+# This code sets the following variables:
+#
+# ::
+#   PYTHONINTERP_FOUND         - if the Python interpret has been found
+#   PYTHON_EXECUTABLE          - the Python interpret found
+#   PYTHON_LIBRARY             - path to the python library
+#   PYTHON_INCLUDE_PATH        - path to where Python.h is found (deprecated)
+#   PYTHON_INCLUDE_DIRS        - path to where Python.h is found
+#   PYTHONLIBS_VERSION_STRING  - version of the Python libs found (since CMake 2.8.8)
+#   PYTHON_VERSION_MAJOR       - major Python version
+#   PYTHON_VERSION_MINOR       - minor Python version
+#   PYTHON_VERSION_PATCH       - patch Python version
+
+
+
+function (findPythonForAnacondaEnvironment env)
+	if (WIN32)
+	  file(TO_CMAKE_PATH ${env}/python.exe PYTHON_EXECUTABLE)
+        elseif (UNIX)
+  	  file(TO_CMAKE_PATH ${env}/bin/python PYTHON_EXECUTABLE)
+	endif()
+
+	
+	message("findPythonForAnacondaEnvironment Found Python Executable" ${PYTHON_EXECUTABLE})
+	####### FROM FindPythonInterpr ########
+	# determine python version string
+	if(PYTHON_EXECUTABLE)
+		execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c
+								"import sys; sys.stdout.write(';'.join([str(x) for x in sys.version_info[:3]]))"
+						OUTPUT_VARIABLE _VERSION
+						RESULT_VARIABLE _PYTHON_VERSION_RESULT
+						ERROR_QUIET)
+		if(NOT _PYTHON_VERSION_RESULT)
+			string(REPLACE ";" "." _PYTHON_VERSION_STRING "${_VERSION}")
+			list(GET _VERSION 0 _PYTHON_VERSION_MAJOR)
+			list(GET _VERSION 1 _PYTHON_VERSION_MINOR)
+			list(GET _VERSION 2 _PYTHON_VERSION_PATCH)
+			if(PYTHON_VERSION_PATCH EQUAL 0)
+				# it's called "Python 2.7", not "2.7.0"
+				string(REGEX REPLACE "\\.0$" "" _PYTHON_VERSION_STRING "${PYTHON_VERSION_STRING}")
+			endif()
+		else()
+			# sys.version predates sys.version_info, so use that
+			execute_process(COMMAND "${PYTHON_EXECUTABLE}" -c "import sys; sys.stdout.write(sys.version)"
+							OUTPUT_VARIABLE _VERSION
+							RESULT_VARIABLE _PYTHON_VERSION_RESULT
+							ERROR_QUIET)
+			if(NOT _PYTHON_VERSION_RESULT)
+				string(REGEX REPLACE " .*" "" _PYTHON_VERSION_STRING "${_VERSION}")
+				string(REGEX REPLACE "^([0-9]+)\\.[0-9]+.*" "\\1" _PYTHON_VERSION_MAJOR "${PYTHON_VERSION_STRING}")
+				string(REGEX REPLACE "^[0-9]+\\.([0-9])+.*" "\\1" _PYTHON_VERSION_MINOR "${PYTHON_VERSION_STRING}")
+				if(PYTHON_VERSION_STRING MATCHES "^[0-9]+\\.[0-9]+\\.([0-9]+)")
+					set(PYTHON_VERSION_PATCH "${CMAKE_MATCH_1}")
+				else()
+					set(PYTHON_VERSION_PATCH "0")
+				endif()
+			else()
+				# sys.version was first documented for Python 1.5, so assume
+				# this is older.
+				set(PYTHON_VERSION_STRING "1.4" PARENT_SCOPE)
+				set(PYTHON_VERSION_MAJOR "1" PARENT_SCOPE)
+				set(PYTHON_VERSION_MINOR "4" PARENT_SCOPE)
+				set(PYTHON_VERSION_PATCH "0" PARENT_SCOPE)
+			endif()
+		endif()
+		unset(_PYTHON_VERSION_RESULT)
+		unset(_VERSION)
+	endif()
+	###############################################
+	
+	set (PYTHON_EXECUTABLE ${PYTHON_EXECUTABLE} PARENT_SCOPE)
+	set (PYTHONINTERP_FOUND "ON" PARENT_SCOPE)
+	set (PYTHON_VERSION_STRING ${_PYTHON_VERSION_STRING} PARENT_SCOPE)
+	set (PYTHON_VERSION_MAJOR ${_PYTHON_VERSION_MAJOR} PARENT_SCOPE)
+	set (PYTHON_VERSION_MINOR ${_PYTHON_VERSION_MINOR} PARENT_SCOPE)
+	set (PYTHON_VERSION_PATCH ${_PYTHON_VERSION_PATCH} PARENT_SCOPE)
+	message("My version found " ${PYTHON_VERSION_STRING})
+	## find conda executable
+	if (WIN32)
+	  set (CONDA_EXECUTABLE ${env}/Script/conda PARENT_SCOPE)
+	elseif(UNIX)
+	  set (CONDA_EXECUTABLE ${env}/bin/conda PARENT_SCOPE)
+	endif()
+endfunction()
+
+
+
+set(Python_ADDITIONAL_VERSIONS 3.5)
+
+find_package(PythonInterp)
+if (PYTHONINTERP_FOUND)
+  
+  message("Found interpret " ${PYTHON_EXECUTABLE})
+  message("Python Library " ${PYTHON_LIBRARY})
+  message("Python Include Dir " ${PYTHON_INCLUDE_DIR})
+  message("Python Include Path " ${PYTHON_INCLUDE_PATH})
+  
+  foreach(pv ${PYTHON_VERSION_STRING})
+    message("Found interpret " ${pv})
+  endforeach()
+endif()
+
+
+
+find_package(PythonLibs)
+if (PYTHONLIB_FOUND) 
+  message("Found PythonLibs PYTHON_LIBRARIES " ${PYTHON_LIBRARIES})
+  message("Found PythonLibs PYTHON_INCLUDE_PATH " ${PYTHON_INCLUDE_PATH})
+  message("Found PythonLibs PYTHON_INCLUDE_DIRS " ${PYTHON_INCLUDE_DIRS})
+  message("Found PythonLibs PYTHONLIBS_VERSION_STRING " ${PYTHONLIBS_VERSION_STRING}  )
+else()
+  message("No PythonLibs Found")  
+endif()
+
+
+
+
+function(findPythonPackagesPath)
+   execute_process(COMMAND ${PYTHON_EXECUTABLE} -c "from distutils.sysconfig import *; print (get_python_lib())"
+                      RESULT_VARIABLE PYTHON_CVPY_PROCESS
+                      OUTPUT_VARIABLE PYTHON_STD_PACKAGES_PATH
+                      OUTPUT_STRIP_TRAILING_WHITESPACE)
+   #message("STD_PACKAGES " ${PYTHON_STD_PACKAGES_PATH})
+   if("${PYTHON_STD_PACKAGES_PATH}" MATCHES "site-packages")
+        set(_PYTHON_PACKAGES_PATH "python${PYTHON_VERSION_MAJOR_MINOR}/site-packages")
+   endif()
+
+    SET(PYTHON_PACKAGES_PATH "${PYTHON_STD_PACKAGES_PATH}" PARENT_SCOPE)
+
+endfunction()
+
+
diff --git a/build/run.sh b/build/run.sh
new file mode 100644
index 0000000..a8e5555
--- /dev/null
+++ b/build/run.sh
@@ -0,0 +1,19 @@
+#!/bin/bash  
+echo "Building CCPi-regularisation Toolkit using CMake"  
+# rm -r build
+# Requires Cython, install it first: 
+# pip install cython
+# mkdir build
+cd build/
+make clean
+# install Python modules only without CUDA
+cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
+# install Python modules only with CUDA
+# cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
+make install
+# cp install/lib/libcilreg.so install/python/ccpi/filters
+cd install/python
+export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../lib
+# spyder
+# one can also run Matlab in Linux as:
+# PATH="/path/to/mex/:$PATH" LD_LIBRARY_PATH="/path/to/library:$LD_LIBRARY_PATH" matlab
diff --git a/docs/data/SinoInpaint.mat b/docs/data/SinoInpaint.mat
new file mode 100644
index 0000000..d748fb4
--- /dev/null
+++ b/docs/data/SinoInpaint.mat
diff --git a/Wrappers/Python/conda-recipe/lena_gray_512.tif b/docs/data/lena_gray_512.tif
index f80cafc..f80cafc 100644
--- a/Wrappers/Python/conda-recipe/lena_gray_512.tif
+++ b/docs/data/lena_gray_512.tif
diff --git a/docs/demos/demoMatlab_3Ddenoise.m b/docs/demos/demoMatlab_3Ddenoise.m
new file mode 100644
index 0000000..0c331a4
--- /dev/null
+++ b/docs/demos/demoMatlab_3Ddenoise.m
@@ -0,0 +1,178 @@
+% Volume (3D) denoising demo using CCPi-RGL
+clear; close all
+Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i);
+Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i);
+Path3 = sprintf(['..' filesep 'supp'], 1i);
+addpath(Path1);
+addpath(Path2);
+addpath(Path3);
+
+N = 512; 
+slices = 7;
+vol3D = zeros(N,N,slices, 'single');
+Ideal3D = zeros(N,N,slices, 'single');
+Im = double(imread('lena_gray_512.tif'))/255;  % loading image
+for i = 1:slices
+vol3D(:,:,i) = Im + .05*randn(size(Im)); 
+Ideal3D(:,:,i) = Im;
+end
+vol3D(vol3D < 0) = 0;
+figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image');
+
+
+lambda_reg = 0.03; % regularsation parameter for all methods
+%%
+fprintf('Denoise a volume using the ROF-TV model (CPU) \n');
+tau_rof = 0.0025; % time-marching constant 
+iter_rof = 300; % number of ROF iterations
+tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; 
+energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1);  % get energy function value
+rmse_rof = (RMSE(Ideal3D(:),u_rof(:)));
+fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof);
+figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the ROF-TV model (GPU) \n');
+% tau_rof = 0.0025; % time-marching constant 
+% iter_rof = 300; % number of ROF iterations
+% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc;
+% rmse_rofG = (RMSE(Ideal3D(:),u_rofG(:)));
+% fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rofG);
+% figure; imshow(u_rofG(:,:,7), [0 1]); title('ROF-TV denoised volume (GPU)');
+%%
+fprintf('Denoise a volume using the FGP-TV model (CPU) \n');
+iter_fgp = 300; % number of FGP iterations
+epsil_tol =  1.0e-05; % tolerance
+tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
+energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg, 1); % get energy function value
+rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:)));
+fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp);
+figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
+% iter_fgp = 300; % number of FGP iterations
+% epsil_tol =  1.0e-05; % tolerance
+% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; 
+% rmse_fgpG = (RMSE(Ideal3D(:),u_fgpG(:)));
+% fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG);
+% figure; imshow(u_fgpG(:,:,7), [0 1]); title('FGP-TV denoised volume (GPU)');
+%%
+fprintf('Denoise a volume using the SB-TV model (CPU) \n');
+iter_sb = 150; % number of SB iterations
+epsil_tol =  1.0e-05; % tolerance
+tic; u_sb = SB_TV(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
+energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg, 1);  % get energy function value
+rmse_sb = (RMSE(Ideal3D(:),u_sb(:)));
+fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sb);
+figure; imshow(u_sb(:,:,7), [0 1]); title('SB-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the SB-TV model (GPU) \n');
+% iter_sb = 150; % number of SB iterations
+% epsil_tol =  1.0e-05; % tolerance
+% tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
+% rmse_sbG = (RMSE(Ideal3D(:),u_sbG(:)));
+% fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sbG);
+% figure; imshow(u_sbG(:,:,7), [0 1]); title('SB-TV denoised volume (GPU)');
+%%
+fprintf('Denoise a volume using the ROF-LLT model (CPU) \n');
+lambda_ROF = lambda_reg; % ROF regularisation parameter
+lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter
+iter_LLT = 300; % iterations 
+tau_rof_llt = 0.0025; % time-marching constant 
+tic; u_rof_llt = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt(:)));
+fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt);
+figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the ROF-LLT model (GPU) \n');
+% lambda_ROF = lambda_reg; % ROF regularisation parameter
+% lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter
+% iter_LLT = 300; % iterations 
+% tau_rof_llt = 0.0025; % time-marching constant 
+% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+% rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt_g(:)));
+% fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt);
+% figure; imshow(u_rof_llt_g(:,:,7), [0 1]); title('ROF-LLT denoised volume (GPU)');
+%%
+fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n');
+iter_diff = 300; % number of diffusion iterations
+lambda_regDiff = 0.025; % regularisation for the diffusivity 
+sigmaPar = 0.015; % edge-preserving parameter
+tau_param = 0.025; % time-marching constant 
+tic; u_diff = NonlDiff(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
+rmse_diff = (RMSE(Ideal3D(:),u_diff(:)));
+fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff);
+figure; imshow(u_diff(:,:,7), [0 1]); title('Diffusion denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using Nonlinear-Diffusion model (GPU) \n');
+% iter_diff = 300; % number of diffusion iterations
+% lambda_regDiff = 0.025; % regularisation for the diffusivity 
+% sigmaPar = 0.015; % edge-preserving parameter
+% tau_param = 0.025; % time-marching constant 
+% tic; u_diff_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
+% rmse_diff = (RMSE(Ideal3D(:),u_diff_g(:)));
+% fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff);
+% figure; imshow(u_diff_g(:,:,7), [0 1]); title('Diffusion denoised volume (GPU)');
+%%
+fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n');
+iter_diff = 300; % number of diffusion iterations
+lambda_regDiff = 3.5; % regularisation for the diffusivity 
+sigmaPar = 0.02; % edge-preserving parameter
+tau_param = 0.0015; % time-marching constant 
+tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
+rmse_diff4 = (RMSE(Ideal3D(:),u_diff4(:)));
+fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4);
+figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CPU)');
+%%
+% fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n');
+% iter_diff = 300; % number of diffusion iterations
+% lambda_regDiff = 3.5; % regularisation for the diffusivity 
+% sigmaPar = 0.02; % edge-preserving parameter
+% tau_param = 0.0015; % time-marching constant 
+% tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
+% rmse_diff4 = (RMSE(Ideal3D(:),u_diff4_g(:)));
+% fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4);
+% figure; imshow(u_diff4_g(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (GPU)');
+%%
+fprintf('Denoise using the TGV model (CPU) \n');
+lambda_TGV = 0.03; % regularisation parameter
+alpha1 = 1.0; % parameter to control the first-order term
+alpha0 = 2.0; % parameter to control the second-order term
+iter_TGV = 500; % number of Primal-Dual iterations for TGV
+tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
+rmseTGV = RMSE(Ideal3D(:),u_tgv(:));
+fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
+figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)');
+%%
+%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
+fprintf('Denoise a volume using the FGP-dTV model (CPU) \n');
+
+% create another volume (reference) with slightly less amount of noise
+vol3D_ref = zeros(N,N,slices, 'single');
+for i = 1:slices
+vol3D_ref(:,:,i) = Im + .01*randn(size(Im)); 
+end
+vol3D_ref(vol3D_ref < 0) = 0;
+% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
+
+iter_fgp = 300; % number of FGP iterations
+epsil_tol =  1.0e-05; % tolerance
+eta =  0.2; % Reference image gradient smoothing constant
+tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
+figure; imshow(u_fgp_dtv(:,:,7), [0 1]); title('FGP-dTV denoised volume (CPU)');
+%%
+fprintf('Denoise a volume using the FGP-dTV model (GPU) \n');
+
+% create another volume (reference) with slightly less amount of noise
+vol3D_ref = zeros(N,N,slices, 'single');
+for i = 1:slices
+vol3D_ref(:,:,i) = Im + .01*randn(size(Im)); 
+end
+vol3D_ref(vol3D_ref < 0) = 0;
+% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
+
+iter_fgp = 300; % number of FGP iterations
+epsil_tol =  1.0e-05; % tolerance
+eta =  0.2; % Reference image gradient smoothing constant
+tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
+figure; imshow(u_fgp_dtv_g(:,:,7), [0 1]); title('FGP-dTV denoised volume (GPU)');
+%%
diff --git a/docs/demos/demoMatlab_denoise.m b/docs/demos/demoMatlab_denoise.m
new file mode 100644
index 0000000..14d3096
--- /dev/null
+++ b/docs/demos/demoMatlab_denoise.m
@@ -0,0 +1,189 @@
+% Image (2D) denoising demo using CCPi-RGL
+clear; close all
+fsep = '/';
+
+Path1 = sprintf(['..' fsep 'mex_compile' fsep 'installed'], 1i);
+Path2 = sprintf(['..' fsep '..' fsep '..' fsep 'data' fsep], 1i);
+Path3 = sprintf(['..' fsep 'supp'], 1i);
+addpath(Path1); addpath(Path2); addpath(Path3);
+
+Im = double(imread('lena_gray_512.tif'))/255;  % loading image
+u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+figure; imshow(u0, [0 1]); title('Noisy image');
+
+lambda_reg = 0.03; % regularsation parameter for all methods
+%%
+fprintf('Denoise using the ROF-TV model (CPU) \n');
+tau_rof = 0.0025; % time-marching constant 
+iter_rof = 750; % number of ROF iterations
+tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; 
+energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1);  % get energy function value
+rmseROF = (RMSE(u_rof(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF);
+figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
+%%
+% fprintf('Denoise using the ROF-TV model (GPU) \n');
+% tau_rof = 0.0025; % time-marching constant 
+% iter_rof = 750; % number of ROF iterations
+% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof); toc;
+% figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
+%%
+fprintf('Denoise using the FGP-TV model (CPU) \n');
+iter_fgp = 1000; % number of FGP iterations
+epsil_tol =  1.0e-06; % tolerance
+tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; 
+energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value
+rmseFGP = (RMSE(u_fgp(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmseFGP);
+figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
+
+%%
+% fprintf('Denoise using the FGP-TV model (GPU) \n');
+% iter_fgp = 1000; % number of FGP iterations
+% epsil_tol =  1.0e-05; % tolerance
+% tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; 
+% figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)');
+%%
+fprintf('Denoise using the SB-TV model (CPU) \n');
+iter_sb = 150; % number of SB iterations
+epsil_tol =  1.0e-06; % tolerance
+tic; u_sb = SB_TV(single(u0), lambda_reg, iter_sb, epsil_tol); toc; 
+energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg, 1);  % get energy function value
+rmseSB = (RMSE(u_sb(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmseSB);
+figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
+%%
+% fprintf('Denoise using the SB-TV model (GPU) \n');
+% iter_sb = 150; % number of SB iterations
+% epsil_tol =  1.0e-06; % tolerance
+% tic; u_sbG = SB_TV_GPU(single(u0), lambda_reg, iter_sb, epsil_tol); toc; 
+% figure; imshow(u_sbG, [0 1]); title('SB-TV denoised image (GPU)');
+%%
+fprintf('Denoise using the TGV model (CPU) \n');
+lambda_TGV = 0.045; % regularisation parameter
+alpha1 = 1.0; % parameter to control the first-order term
+alpha0 = 2.0; % parameter to control the second-order term
+iter_TGV = 2000; % number of Primal-Dual iterations for TGV
+tic; u_tgv = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
+rmseTGV = (RMSE(u_tgv(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
+figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
+%%
+% fprintf('Denoise using the TGV model (GPU) \n');
+% lambda_TGV = 0.045; % regularisation parameter
+% alpha1 = 1.0; % parameter to control the first-order term
+% alpha0 = 2.0; % parameter to control the second-order term
+% iter_TGV = 2000; % number of Primal-Dual iterations for TGV
+% tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
+% rmseTGV_gpu = (RMSE(u_tgv_gpu(:),Im(:)));
+% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV_gpu);
+% figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)');
+%%
+fprintf('Denoise using the ROF-LLT model (CPU) \n');
+lambda_ROF = lambda_reg; % ROF regularisation parameter
+lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter
+iter_LLT = 1; % iterations 
+tau_rof_llt = 0.0025; % time-marching constant 
+tic; u_rof_llt = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+rmseROFLLT = (RMSE(u_rof_llt(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT);
+figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)');
+%%
+% fprintf('Denoise using the ROF-LLT model (GPU) \n');
+% lambda_ROF = lambda_reg; % ROF regularisation parameter
+% lambda_LLT = lambda_reg*0.45; % LLT regularisation parameter
+% iter_LLT = 500; % iterations 
+% tau_rof_llt = 0.0025; % time-marching constant 
+% tic; u_rof_llt_g = LLT_ROF_GPU(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; 
+% rmseROFLLT_g = (RMSE(u_rof_llt_g(:),Im(:)));
+% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT_g);
+% figure; imshow(u_rof_llt_g, [0 1]); title('ROF-LLT denoised image (GPU)');
+%%
+fprintf('Denoise using Nonlinear-Diffusion model (CPU) \n');
+iter_diff = 800; % number of diffusion iterations
+lambda_regDiff = 0.025; % regularisation for the diffusivity 
+sigmaPar = 0.015; % edge-preserving parameter
+tau_param = 0.025; % time-marching constant 
+tic; u_diff = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
+rmseDiffus = (RMSE(u_diff(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus);
+figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)');
+%%
+% fprintf('Denoise using Nonlinear-Diffusion model (GPU) \n');
+% iter_diff = 800; % number of diffusion iterations
+% lambda_regDiff = 0.025; % regularisation for the diffusivity 
+% sigmaPar = 0.015; % edge-preserving parameter
+% tau_param = 0.025; % time-marching constant 
+% tic; u_diff_g = NonlDiff_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
+% figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)');
+%%
+fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n');
+iter_diff = 800; % number of diffusion iterations
+lambda_regDiff = 3.5; % regularisation for the diffusivity 
+sigmaPar = 0.02; % edge-preserving parameter
+tau_param = 0.0015; % time-marching constant 
+tic; u_diff4 = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
+rmseDiffHO = (RMSE(u_diff4(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rmseDiffHO);
+figure; imshow(u_diff4, [0 1]); title('Diffusion 4thO denoised image (CPU)');
+%%
+% fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n');
+% iter_diff = 800; % number of diffusion iterations
+% lambda_regDiff = 3.5; % regularisation for the diffusivity 
+% sigmaPar = 0.02; % edge-preserving parameter
+% tau_param = 0.0015; % time-marching constant 
+% tic; u_diff4_g = Diffusion_4thO_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
+% figure; imshow(u_diff4_g, [0 1]); title('Diffusion 4thO denoised image (GPU)');
+%%
+fprintf('Weights pre-calculation for Non-local TV (takes time on CPU) \n');
+SearchingWindow = 7;
+PatchWindow = 2;
+NeighboursNumber = 20; % the number of neibours to include
+h = 0.23; % edge related parameter for NLM
+tic; [H_i, H_j, Weights] = PatchSelect(single(u0), SearchingWindow, PatchWindow, NeighboursNumber, h); toc;
+%%
+fprintf('Denoise using Non-local Total Variation (CPU) \n');
+iter_nltv = 3; % number of nltv iterations
+lambda_nltv = 0.05; % regularisation parameter for nltv
+tic; u_nltv = Nonlocal_TV(single(u0), H_i, H_j, 0, Weights, lambda_nltv, iter_nltv); toc; 
+rmse_nltv = (RMSE(u_nltv(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for Non-local Total Variation is:', rmse_nltv);
+figure; imagesc(u_nltv, [0 1]); colormap(gray); daspect([1 1 1]); title('Non-local Total Variation denoised image (CPU)');
+%%
+%>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
+
+fprintf('Denoise using the FGP-dTV model (CPU) \n');
+% create another image (reference) with slightly less amount of noise
+u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0;
+% u_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
+
+iter_fgp = 1000; % number of FGP iterations
+epsil_tol =  1.0e-06; % tolerance
+eta =  0.2; % Reference image gradient smoothing constant
+tic; u_fgp_dtv = FGP_dTV(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
+rmse_dTV= (RMSE(u_fgp_dtv(:),Im(:)));
+fprintf('%s %f \n', 'RMSE error for Directional Total Variation (dTV) is:', rmse_dTV);
+figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)');
+%%
+% fprintf('Denoise using the FGP-dTV model (GPU) \n');
+% % create another image (reference) with slightly less amount of noise
+% u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0;
+% % u_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
+% 
+% iter_fgp = 1000; % number of FGP iterations
+% epsil_tol =  1.0e-06; % tolerance
+% eta =  0.2; % Reference image gradient smoothing constant
+% tic; u_fgp_dtvG = FGP_dTV_GPU(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
+% figure; imshow(u_fgp_dtvG, [0 1]); title('FGP-dTV denoised image (GPU)');
+%%
+fprintf('Denoise using the TNV prior (CPU) \n');
+slices = 5; N = 512;
+vol3D = zeros(N,N,slices, 'single');
+for i = 1:slices
+vol3D(:,:,i) = Im + .05*randn(size(Im)); 
+end
+vol3D(vol3D < 0) = 0;
+
+iter_tnv = 200; % number of TNV iterations
+tic; u_tnv = TNV(single(vol3D), lambda_reg, iter_tnv); toc; 
+figure; imshow(u_tnv(:,:,3), [0 1]); title('TNV denoised stack of channels (CPU)');
diff --git a/docs/demos/demoMatlab_inpaint.m b/docs/demos/demoMatlab_inpaint.m
new file mode 100644
index 0000000..66f9c15
--- /dev/null
+++ b/docs/demos/demoMatlab_inpaint.m
@@ -0,0 +1,35 @@
+% Image (2D) inpainting demo using CCPi-RGL
+clear; close all
+Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i);
+Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i);
+addpath(Path1);
+addpath(Path2);
+
+load('SinoInpaint.mat');
+Sinogram = Sinogram./max(Sinogram(:));
+Sino_mask = Sinogram.*(1-single(Mask));
+figure; 
+subplot(1,2,1); imshow(Sino_mask, [0 1]); title('Missing data sinogram');
+subplot(1,2,2); imshow(Mask, [0 1]); title('Mask');
+%%
+fprintf('Inpaint using Linear-Diffusion model (CPU) \n');
+iter_diff = 5000; % number of diffusion iterations
+lambda_regDiff = 6000; % regularisation for the diffusivity 
+sigmaPar = 0.0; % edge-preserving parameter
+tau_param = 0.000075; % time-marching constant 
+tic; u_diff = NonlDiff_Inp(single(Sino_mask), Mask, lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
+figure; imshow(u_diff, [0 1]); title('Linear-Diffusion inpainted sinogram (CPU)');
+%%
+fprintf('Inpaint using Nonlinear-Diffusion model (CPU) \n');
+iter_diff = 1500; % number of diffusion iterations
+lambda_regDiff = 80; % regularisation for the diffusivity 
+sigmaPar = 0.00009; % edge-preserving parameter
+tau_param = 0.000008; % time-marching constant 
+tic; u_diff = NonlDiff_Inp(single(Sino_mask), Mask, lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
+figure; imshow(u_diff, [0 1]); title('Non-Linear Diffusion inpainted sinogram (CPU)');
+%%
+fprintf('Inpaint using Nonlocal Vertical Marching model (CPU) \n');
+Increment = 1; % linear increment for the searching window
+tic; [u_nom,maskupd] = NonlocalMarching_Inpaint(single(Sino_mask), Mask, Increment); toc;
+figure; imshow(u_nom, [0 1]); title('NVM inpainted sinogram (CPU)');
+%%
\ No newline at end of file
diff --git a/docs/demos/demo_cpu_inpainters.py b/docs/demos/demo_cpu_inpainters.py
new file mode 100644
index 0000000..3b4191b
--- /dev/null
+++ b/docs/demos/demo_cpu_inpainters.py
@@ -0,0 +1,192 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Demonstration of CPU inpainters
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from scipy import io
+from ccpi.filters.regularisers import NDF_INP, NVM_INP
+from qualitymetrics import rmse
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'maskData':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+
+# read sinogram and the mask
+filename = os.path.join(".." , ".." , ".." , "data" ,"SinoInpaint.mat")
+sino = io.loadmat(filename)
+sino_full = sino.get('Sinogram')
+Mask = sino.get('Mask')
+[angles_dim,detectors_dim] = sino_full.shape
+sino_full = sino_full/np.max(sino_full)
+#apply mask to sinogram
+sino_cut = sino_full*(1-Mask)
+#sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32')
+#sino_cut_new = sino_cut.copy(order='c')
+#sino_cut_new[:] = sino_cut[:]
+sino_cut_new = np.ascontiguousarray(sino_cut, dtype=np.float32);
+#mask = np.zeros((angles_dim,detectors_dim),'uint8')
+#mask =Mask.copy(order='c')
+#mask[:] = Mask[:]
+mask = np.ascontiguousarray(Mask, dtype=np.uint8);
+
+plt.figure(1)
+plt.subplot(121)
+plt.imshow(sino_cut_new,vmin=0.0, vmax=1)
+plt.title('Missing Data sinogram')
+plt.subplot(122)
+plt.imshow(mask)
+plt.title('Mask')
+plt.show()
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Inpainting using linear diffusion (2D)__")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(2)
+plt.suptitle('Performance of linear inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut_new,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+        'input' : sino_cut_new,\
+        'maskData' : mask,\
+        'regularisation_parameter':5000,\
+        'edge_parameter':0,\
+        'number_of_iterations' :5000 ,\
+        'time_marching_parameter':0.000075,\
+        'penalty_type':0
+        }
+        
+start_time = timeit.default_timer()
+ndf_inp_linear = NDF_INP(pars['input'],
+              pars['maskData'],
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])
+             
+rms = rmse(sino_full, ndf_inp_linear)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_inp_linear, cmap="gray")
+plt.title('{}'.format('Linear diffusion inpainting results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_Inpainting using nonlinear diffusion (2D)_")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(3)
+plt.suptitle('Performance of nonlinear diffusion inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut_new,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+        'input' : sino_cut_new,\
+        'maskData' : mask,\
+        'regularisation_parameter':80,\
+        'edge_parameter':0.00009,\
+        'number_of_iterations' :1500 ,\
+        'time_marching_parameter':0.000008,\
+        'penalty_type':1
+        }
+        
+start_time = timeit.default_timer()
+ndf_inp_nonlinear = NDF_INP(pars['input'],
+              pars['maskData'],
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])
+             
+rms = rmse(sino_full, ndf_inp_nonlinear)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_inp_nonlinear, cmap="gray")
+plt.title('{}'.format('Nonlinear diffusion inpainting results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Inpainting using nonlocal vertical marching")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(4)
+plt.suptitle('Performance of NVM inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NVM_INP, \
+        'input' : sino_cut_new,\
+        'maskData' : mask,\
+        'SW_increment': 1,\
+        'number_of_iterations' : 150
+        }
+        
+start_time = timeit.default_timer()
+(nvm_inp, mask_upd) = NVM_INP(pars['input'],
+              pars['maskData'],
+              pars['SW_increment'],
+              pars['number_of_iterations'])
+             
+rms = rmse(sino_full, nvm_inp)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(nvm_inp, cmap="gray")
+plt.title('{}'.format('Nonlocal Vertical Marching inpainting results'))
+#%%
diff --git a/docs/demos/demo_cpu_regularisers.py b/docs/demos/demo_cpu_regularisers.py
new file mode 100644
index 0000000..e6befa9
--- /dev/null
+++ b/docs/demos/demo_cpu_regularisers.py
@@ -0,0 +1,572 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of CPU regularisers 
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, TNV, NDF, Diff4th
+from ccpi.filters.regularisers import PatchSelect, NLTV
+from qualitymetrics import rmse
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255.0
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+
+# change dims to check that modules work with non-squared images
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________ROF-TV (2D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of ROF-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 1200,\
+        'time_marching_parameter': 0.0025        
+        }
+print ("#############ROF TV CPU####################")
+start_time = timeit.default_timer()
+rof_cpu = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+rms = rmse(Im, rof_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-TV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP TV CPU####################")
+start_time = timeit.default_timer()
+fgp_cpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')  
+             
+             
+rms = rmse(Im, fgp_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________SB-TV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of SB-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############SB TV CPU####################")
+start_time = timeit.default_timer()
+sb_cpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'cpu')  
+             
+             
+rms = rmse(Im, sb_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____Total Generalised Variation (2D)______")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :1350 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+             
+rms = rmse(Im, tgv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("______________LLT- ROF (2D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+        
+print ("#############LLT- ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+rms = rmse(Im, lltrof_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("________________NDF (2D)___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NDF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : u0,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':1
+        }
+        
+print ("#############NDF CPU################")
+start_time = timeit.default_timer()
+ndf_cpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'cpu')  
+             
+rms = rmse(Im, ndf_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of Diff4th regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : u0,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############Diff4th CPU################")
+start_time = timeit.default_timer()
+diff4_cpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+             
+rms = rmse(Im, diff4_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal patches pre-calculation____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+start_time = timeit.default_timer()
+# set parameters
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+H_i, H_j, Weights = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'cpu')
+              
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+"""
+plt.figure()
+plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
+plt.show()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal Total Variation penalty____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NLTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars2 = {'algorithm' : NLTV, \
+        'input' : u0,\
+        'H_i': H_i, \
+        'H_j': H_j,\
+        'H_k' : 0,\
+        'Weights' : Weights,\
+        'regularisation_parameter': 0.04,\
+        'iterations': 3
+        }
+start_time = timeit.default_timer()
+nltv_cpu = NLTV(pars2['input'], 
+              pars2['H_i'],
+              pars2['H_j'], 
+              pars2['H_k'],
+              pars2['Weights'],
+              pars2['regularisation_parameter'],
+              pars2['iterations'])
+
+rms = rmse(Im, nltv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(nltv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____________FGP-dTV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dtv_cpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+rms = rmse(Im, fgp_dtv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("__________Total nuclear Variation__________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TNV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+channelsNo = 5
+noisyVol = np.zeros((channelsNo,N,M),dtype='float32')
+idealVol = np.zeros((channelsNo,N,M),dtype='float32')
+
+for i in range (channelsNo):
+    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    idealVol[i,:,:] = Im
+
+# set parameters
+pars = {'algorithm' : TNV, \
+        'input' : noisyVol,\
+        'regularisation_parameter': 0.04, \
+        'number_of_iterations' : 200 ,\
+        'tolerance_constant':1e-05
+        }
+        
+print ("#############TNV CPU#################")
+start_time = timeit.default_timer()
+tnv_cpu = TNV(pars['input'],           
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'])
+             
+rms = rmse(idealVol, tnv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tnv_cpu[3,:,:], cmap="gray")
+plt.title('{}'.format('CPU results'))
diff --git a/docs/demos/demo_cpu_regularisers3D.py b/docs/demos/demo_cpu_regularisers3D.py
new file mode 100644
index 0000000..2d2fc22
--- /dev/null
+++ b/docs/demos/demo_cpu_regularisers3D.py
@@ -0,0 +1,458 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of 3D CPU regularisers 
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from qualitymetrics import rmse
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+
+# change dims to check that modules work with non-squared images
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+slices = 15
+
+noisyVol = np.zeros((slices,N,M),dtype='float32')
+noisyRef = np.zeros((slices,N,M),dtype='float32')
+idealVol = np.zeros((slices,N,M),dtype='float32')
+
+for i in range (slices):
+    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
+    idealVol[i,:,:] = Im
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________ROF-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of ROF-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy 15th slice of a volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 500,\
+        'time_marching_parameter': 0.0025
+        }
+print ("#############ROF TV CPU####################")
+start_time = timeit.default_timer()
+rof_cpu3D = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+rms = rmse(idealVol, rof_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using ROF-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-TV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP TV CPU####################")
+start_time = timeit.default_timer()
+fgp_cpu3D = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')  
+             
+             
+rms = rmse(idealVol, fgp_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using FGP-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________SB-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of SB-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############SB TV CPU####################")
+start_time = timeit.default_timer()
+sb_cpu3D = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'cpu')
+             
+rms = rmse(idealVol, sb_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using SB-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________LLT-ROF (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : noisyVol,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.015, \
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+
+print ("#############LLT ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu3D = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+rms = rmse(idealVol, lltrof_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using LLT-ROF'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________TGV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :250 ,\
+        'LipshitzConstant' :12 ,\
+        }
+
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu3D = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+
+rms = rmse(idealVol, tgv_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using TGV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("________________NDF (3D)___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NDF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+        
+print ("#############NDF CPU################")
+start_time = timeit.default_timer()
+ndf_cpu3D = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])  
+             
+rms = rmse(idealVol, ndf_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using NDF iterations'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of Diff4th regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : noisyVol,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############Diff4th CPU################")
+start_time = timeit.default_timer()
+diff4th_cpu3D = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'])  
+             
+rms = rmse(idealVol, diff4th_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4th_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using DIFF4th iterations'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-dTV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV,\
+        'input' : noisyVol,\
+        'refdata' : noisyRef,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dTV_cpu3D = FGP_dTV(pars['input'],
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'],
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+             
+rms = rmse(idealVol, fgp_dTV_cpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dTV_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using FGP-dTV'))
+#%%
diff --git a/docs/demos/demo_cpu_vs_gpu_regularisers.py b/docs/demos/demo_cpu_vs_gpu_regularisers.py
new file mode 100644
index 0000000..230a761
--- /dev/null
+++ b/docs/demos/demo_cpu_vs_gpu_regularisers.py
@@ -0,0 +1,790 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of CPU implementation against the GPU one
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.filters.regularisers import PatchSelect
+from qualitymetrics import rmse
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+
+filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)                     
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________ROF-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of ROF-TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 4500,\
+        'time_marching_parameter': 0.00002
+        }
+print ("#############ROF TV CPU####################")
+start_time = timeit.default_timer()
+rof_cpu = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+rms = rmse(Im, rof_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############ROF TV GPU##################")
+start_time = timeit.default_timer()
+rof_gpu = ROF_TV(pars['input'], 
+                     pars['regularisation_parameter'],
+                     pars['number_of_iterations'], 
+                     pars['time_marching_parameter'],'gpu')
+                     
+rms = rmse(Im, rof_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = ROF_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(rof_cpu))
+diff_im = abs(rof_cpu - rof_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of FGP-TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :1200 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP TV CPU####################")
+start_time = timeit.default_timer()
+fgp_cpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')  
+             
+             
+rms = rmse(Im, fgp_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############FGP TV GPU##################")
+start_time = timeit.default_timer()
+fgp_gpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, fgp_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = FGP_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(fgp_cpu))
+diff_im = abs(fgp_cpu - fgp_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________SB-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of SB-TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':1e-05,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############SB-TV CPU####################")
+start_time = timeit.default_timer()
+sb_cpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'cpu')  
+             
+             
+rms = rmse(Im, sb_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############SB TV GPU##################")
+start_time = timeit.default_timer()
+sb_gpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, sb_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = SB_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(sb_cpu))
+diff_im = abs(sb_cpu - sb_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________TGV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of TGV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :400 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+rms = rmse(Im, tgv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############TGV GPU##################")
+start_time = timeit.default_timer()
+tgv_gpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')
+                                   
+rms = rmse(Im, tgv_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = TGV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(tgv_gpu))
+diff_im = abs(tgv_cpu - tgv_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________LLT-ROF bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of LLT-ROF regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :4500 ,\
+        'time_marching_parameter' :0.00002 ,\
+        }
+        
+print ("#############LLT- ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+rms = rmse(Im, lltrof_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("#############LLT- ROF GPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+
+rms = rmse(Im, lltrof_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = LLT_ROF
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(lltrof_gpu))
+diff_im = abs(lltrof_cpu - lltrof_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________NDF bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of NDF regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : u0,\
+        'regularisation_parameter':0.06, \
+        'edge_parameter':0.04,\
+        'number_of_iterations' :1000 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+        
+print ("#############NDF CPU####################")
+start_time = timeit.default_timer()
+ndf_cpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'cpu')
+             
+rms = rmse(Im, ndf_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############NDF GPU##################")
+start_time = timeit.default_timer()
+ndf_gpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'gpu')
+             
+rms = rmse(Im, ndf_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = NDF
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(ndf_cpu))
+diff_im = abs(ndf_cpu - ndf_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of Diff4th regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : u0,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.001
+        }
+
+print ("#############Diff4th CPU####################")
+start_time = timeit.default_timer()
+diff4th_cpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+             
+rms = rmse(Im, diff4th_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4th_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############Diff4th GPU##################")
+start_time = timeit.default_timer()
+diff4th_gpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 'gpu')
+             
+rms = rmse(Im, diff4th_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = Diff4th
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4th_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(diff4th_cpu))
+diff_im = abs(diff4th_cpu - diff4th_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-dTV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :1000 ,\
+        'tolerance_constant':1e-07,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dtv_cpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+             
+rms = rmse(Im, fgp_dtv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############FGP dTV GPU##################")
+start_time = timeit.default_timer()
+fgp_dtv_gpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+rms = rmse(Im, fgp_dtv_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = FGP_dTV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(fgp_dtv_cpu))
+diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____Non-local regularisation bench_________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of Nonlocal TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+print ("############## Nonlocal Patches on CPU##################")
+start_time = timeit.default_timer()
+H_i, H_j, WeightsCPU = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'cpu')
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+
+print ("############## Nonlocal Patches on GPU##################")
+start_time = timeit.default_timer()
+start_time = timeit.default_timer()
+H_i, H_j, WeightsGPU = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'gpu')
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(u0))
+diff_im = abs(WeightsCPU[0,:,:] - WeightsGPU[0,:,:])
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,2,2)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
\ No newline at end of file
diff --git a/docs/demos/demo_gpu_regularisers.py b/docs/demos/demo_gpu_regularisers.py
new file mode 100644
index 0000000..e1c6575
--- /dev/null
+++ b/docs/demos/demo_gpu_regularisers.py
@@ -0,0 +1,518 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of GPU regularisers
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.filters.regularisers import PatchSelect, NLTV
+from qualitymetrics import rmse
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)                     
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________ROF-TV regulariser_____________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the ROF-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 1200,\
+        'time_marching_parameter': 0.0025
+        }
+print ("##############ROF TV GPU##################")
+start_time = timeit.default_timer()
+rof_gpu = ROF_TV(pars['input'], 
+                     pars['regularisation_parameter'],
+                     pars['number_of_iterations'], 
+                     pars['time_marching_parameter'],'gpu')
+                     
+rms = rmse(Im, rof_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = ROF_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-TV regulariser_____________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the FGP-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :1200 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("##############FGP TV GPU##################")
+start_time = timeit.default_timer()
+fgp_gpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, fgp_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = FGP_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________SB-TV regulariser______________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the SB-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("##############SB TV GPU##################")
+start_time = timeit.default_timer()
+sb_gpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, sb_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = SB_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____Total Generalised Variation (2D)______")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :1250 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_gpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')  
+             
+             
+rms = rmse(Im, tgv_gpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("______________LLT- ROF (2D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+        
+print ("#############LLT- ROF GPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+             
+             
+rms = rmse(Im, lltrof_gpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________NDF regulariser_____________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the NDF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : u0,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+
+print ("##############NDF GPU##################")
+start_time = timeit.default_timer()
+ndf_gpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'gpu')  
+             
+rms = rmse(Im, ndf_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = NDF
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of Diff4th regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : u0,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############DIFF4th CPU################")
+start_time = timeit.default_timer()
+diff4_gpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+             
+rms = rmse(Im, diff4_gpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal patches pre-calculation____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+start_time = timeit.default_timer()
+# set parameters
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+H_i, H_j, Weights = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'gpu')
+              
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+"""
+plt.figure()
+plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
+plt.show()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal Total Variation penalty____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NLTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars2 = {'algorithm' : NLTV, \
+        'input' : u0,\
+        'H_i': H_i, \
+        'H_j': H_j,\
+        'H_k' : 0,\
+        'Weights' : Weights,\
+        'regularisation_parameter': 0.02,\
+        'iterations': 3
+        }
+start_time = timeit.default_timer()
+nltv_cpu = NLTV(pars2['input'], 
+              pars2['H_i'],
+              pars2['H_j'], 
+              pars2['H_k'],
+              pars2['Weights'],
+              pars2['regularisation_parameter'],
+              pars2['iterations'])
+
+rms = rmse(Im, nltv_cpu)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(nltv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-dTV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the FGP-dTV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("##############FGP dTV GPU##################")
+start_time = timeit.default_timer()
+fgp_dtv_gpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+                                   
+rms = rmse(Im, fgp_dtv_gpu)
+pars['rmse'] = rms
+pars['algorithm'] = FGP_dTV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
diff --git a/docs/demos/demo_gpu_regularisers3D.py b/docs/demos/demo_gpu_regularisers3D.py
new file mode 100644
index 0000000..b6058d2
--- /dev/null
+++ b/docs/demos/demo_gpu_regularisers3D.py
@@ -0,0 +1,460 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of GPU regularisers
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from qualitymetrics import rmse
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)                     
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+
+
+slices = 20
+
+filename = os.path.join(".." , ".." , ".." , "data" ,"lena_gray_512.tif")
+Im = plt.imread(filename)
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+
+noisyVol = np.zeros((slices,N,N),dtype='float32')
+noisyRef = np.zeros((slices,N,N),dtype='float32')
+idealVol = np.zeros((slices,N,N),dtype='float32')
+
+for i in range (slices):
+    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
+    idealVol[i,:,:] = Im
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________ROF-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of ROF-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy 15th slice of a volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 500,\
+        'time_marching_parameter': 0.0025        
+        }
+print ("#############ROF TV GPU####################")
+start_time = timeit.default_timer()
+rof_gpu3D = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'gpu')
+rms = rmse(idealVol, rof_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using ROF-TV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-TV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("#############FGP TV GPU####################")
+start_time = timeit.default_timer()
+fgp_gpu3D = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+
+rms = rmse(idealVol, fgp_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using FGP-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________SB-TV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of SB-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :100 ,\
+        'tolerance_constant':1e-05,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("#############SB TV GPU####################")
+start_time = timeit.default_timer()
+sb_gpu3D = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'gpu')
+
+rms = rmse(idealVol, sb_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using SB-TV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________LLT-ROF (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : noisyVol,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.015, \
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+
+print ("#############LLT ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu3D = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+
+rms = rmse(idealVol, lltrof_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using LLT-ROF'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________TGV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :600 ,\
+        'LipshitzConstant' :12 ,\
+        }
+
+print ("#############TGV GPU####################")
+start_time = timeit.default_timer()
+tgv_gpu3D = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')
+             
+
+rms = rmse(idealVol, tgv_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using TGV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________NDF-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NDF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+
+print ("#############NDF GPU####################")
+start_time = timeit.default_timer()
+ndf_gpu3D = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'gpu')
+
+rms = rmse(idealVol, ndf_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using NDF'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (3D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of DIFF4th regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : noisyVol,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############DIFF4th CPU################")
+start_time = timeit.default_timer()
+diff4_gpu3D = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+             
+rms = rmse(idealVol, diff4_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-dTV (3D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-dTV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : noisyVol,\
+        'refdata' : noisyRef,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("#############FGP TV GPU####################")
+start_time = timeit.default_timer()
+fgp_dTV_gpu3D = FGP_dTV(pars['input'],
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'],
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+
+rms = rmse(idealVol, fgp_dTV_gpu3D)
+pars['rmse'] = rms
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dTV_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using FGP-dTV'))
+#%%
diff --git a/docs/demos/qualitymetrics.py b/docs/demos/qualitymetrics.py
new file mode 100644
index 0000000..850829e
--- /dev/null
+++ b/docs/demos/qualitymetrics.py
@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Feb 21 13:34:32 2018
+# quality metrics
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+import numpy as np
+
+def nrmse(im1, im2):
+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size))
+    max_val = max(np.max(im1), np.max(im2))
+    min_val = min(np.min(im1), np.min(im2))
+    return 1 - (rmse / (max_val - min_val))
+    
+def rmse(im1, im2):
+    rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size))
+    return rmse
diff --git a/recipe/bld.bat b/recipe/bld.bat
new file mode 100644
index 0000000..6c84355
--- /dev/null
+++ b/recipe/bld.bat
@@ -0,0 +1,20 @@
+IF NOT DEFINED CIL_VERSION (
+ECHO CIL_VERSION Not Defined.
+exit 1
+)
+
+mkdir "%SRC_DIR%\ccpi"
+ROBOCOPY /E "%RECIPE_DIR%\..\.." "%SRC_DIR%\ccpi"
+ROBOCOPY /E "%RECIPE_DIR%\..\..\..\Core" "%SRC_DIR%\Core"
+::cd %SRC_DIR%\ccpi\Python
+cd %SRC_DIR%
+
+:: issue cmake to create setup.py
+cmake -G "NMake Makefiles" %RECIPE_DIR%\..\..\..\ -DBUILD_PYTHON_WRAPPERS=ON -DCONDA_BUILD=ON -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB="%CONDA_PREFIX%\lib" -DLIBRARY_INC="%CONDA_PREFIX%" -DCMAKE_INSTALL_PREFIX="%PREFIX%\Library" 
+
+::%PYTHON% setup-regularisers.py build_ext
+::if errorlevel 1 exit 1
+::%PYTHON% setup-regularisers.py install
+::if errorlevel 1 exit 1
+nmake install
+if errorlevel 1 exit 1
\ No newline at end of file
diff --git a/recipe/build.sh b/recipe/build.sh
new file mode 100644
index 0000000..1d54b6f
--- /dev/null
+++ b/recipe/build.sh
@@ -0,0 +1,18 @@
+
+mkdir "$SRC_DIR/ccpi"
+cp -rv "$RECIPE_DIR/../src/Matlab" "$SRC_DIR/ccpi"
+cp -rv "$RECIPE_DIR/../src/Python" "$SRC_DIR/ccpi"
+cp -rv "$RECIPE_DIR/../src/Core" "$SRC_DIR/Core"
+
+cd $SRC_DIR
+##cuda=off
+
+cmake -G "Unix Makefiles" $RECIPE_DIR/../ -DBUILD_PYTHON_WRAPPER=ON -DCONDA_BUILD=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE="Release" -DLIBRARY_LIB=$CONDA_PREFIX/lib -DLIBRARY_INC=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$PREFIX
+
+
+make install
+
+#$PYTHON setup-regularisers.py build_ext
+#$PYTHON setup-regularisers.py install
+
+
diff --git a/recipe/conda_build_config.yaml b/recipe/conda_build_config.yaml
new file mode 100644
index 0000000..fbe82dc
--- /dev/null
+++ b/recipe/conda_build_config.yaml
@@ -0,0 +1,9 @@
+python:
+  - 2.7 # [not win]
+  - 3.5
+  - 3.6
+#  - 3.7
+numpy:
+  - 1.12
+  - 1.14
+  - 1.15
diff --git a/recipe/meta.yaml b/recipe/meta.yaml
new file mode 100644
index 0000000..7435b2b
--- /dev/null
+++ b/recipe/meta.yaml
@@ -0,0 +1,40 @@
+package:
+  name: ccpi-regulariser
+  version: {{CIL_VERSION}}
+  
+build:
+  preserve_egg_dir: False
+  number: 0
+  script_env:
+    - CIL_VERSION
+  
+test:
+  files:
+    - lena_gray_512.tif
+  requires:
+    - pillow=4.1.1
+
+requirements:
+  build:
+    - python
+    - numpy {{ numpy }}
+    - setuptools
+    - cython
+    - vc 14 # [win and py36] 
+    - vc 14 # [win and py35] 
+    - vc 9  # [win and py27]
+    - cmake 
+
+  run:
+    - {{ pin_compatible('numpy', max_pin='x.x') }}
+    - python
+    - numpy
+    - vc 14 # [win and py36] 
+    - vc 14 # [win and py35] 
+    - vc 9  # [win and py27]
+    - libgcc-ng
+
+about:
+  home: http://www.ccpi.ac.uk
+  license:  BSD license
+  summary: 'CCPi Core Imaging Library Quantification Toolbox'
diff --git a/recipe/run_test.py b/recipe/run_test.py
new file mode 100755
index 0000000..21f3216
--- /dev/null
+++ b/recipe/run_test.py
@@ -0,0 +1,819 @@
+import unittest

+import numpy as np

+import os

+import timeit

+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th

+from PIL import Image

+

+class TiffReader(object):

+    def imread(self, filename):

+        return np.asarray(Image.open(filename))

+###############################################################################

+def printParametersToString(pars):

+        txt = r''

+        for key, value in pars.items():

+            if key== 'algorithm' :

+                txt += "{0} = {1}".format(key, value.__name__)

+            elif key == 'input':

+                txt += "{0} = {1}".format(key, np.shape(value))

+            elif key == 'refdata':

+                txt += "{0} = {1}".format(key, np.shape(value))

+            else:

+                txt += "{0} = {1}".format(key, value)

+            txt += '\n'

+        return txt

+def nrmse(im1, im2):

+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size))

+    max_val = max(np.max(im1), np.max(im2))

+    min_val = min(np.min(im1), np.min(im2))

+    return 1 - (rmse / (max_val - min_val))

+    

+def rmse(im1, im2):

+    rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size))

+    return rmse

+###############################################################################

+

+class TestRegularisers(unittest.TestCase):

+    

+

+    def test_ROF_TV_CPU_vs_GPU(self):

+        #print ("tomas debug test function")

+        print(__name__)

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("____________ROF-TV bench___________________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        # set parameters

+        pars = {'algorithm': ROF_TV, \

+        'input' : u0,\

+        'regularisation_parameter':0.04,\

+        'number_of_iterations': 2500,\

+        'time_marching_parameter': 0.00002

+        }

+        print ("#############ROF TV CPU####################")

+        start_time = timeit.default_timer()

+        rof_cpu = ROF_TV(pars['input'],

+                     pars['regularisation_parameter'],

+                     pars['number_of_iterations'],

+                     pars['time_marching_parameter'],'cpu')

+        rms = rmse(Im, rof_cpu)

+        pars['rmse'] = rms

+        

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("##############ROF TV GPU##################")

+        start_time = timeit.default_timer()

+        try:

+            rof_gpu = ROF_TV(pars['input'], 

+                             pars['regularisation_parameter'],

+                             pars['number_of_iterations'], 

+                             pars['time_marching_parameter'],'gpu')

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+

+        rms = rmse(Im, rof_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = ROF_TV

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("--------Compare the results--------")

+        tolerance = 1e-04

+        diff_im = np.zeros(np.shape(rof_cpu))

+        diff_im = abs(rof_cpu - rof_gpu)

+        diff_im[diff_im > tolerance] = 1

+        self.assertLessEqual(diff_im.sum() , 1)

+        

+    def test_FGP_TV_CPU_vs_GPU(self):

+        print(__name__)

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("____________FGP-TV bench___________________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        

+        # set parameters

+        pars = {'algorithm' : FGP_TV, \

+                'input' : u0,\

+                'regularisation_parameter':0.04, \

+                'number_of_iterations' :1200 ,\

+                'tolerance_constant':0.00001,\

+                'methodTV': 0 ,\

+                'nonneg': 0 ,\

+                'printingOut': 0 

+                }

+                

+        print ("#############FGP TV CPU####################")

+        start_time = timeit.default_timer()

+        fgp_cpu = FGP_TV(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['number_of_iterations'],

+                      pars['tolerance_constant'], 

+                      pars['methodTV'],

+                      pars['nonneg'],

+                      pars['printingOut'],'cpu')  

+                     

+                     

+        rms = rmse(Im, fgp_cpu)

+        pars['rmse'] = rms

+        

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        

+        print ("##############FGP TV GPU##################")

+        start_time = timeit.default_timer()

+        try:

+            fgp_gpu = FGP_TV(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['number_of_iterations'],

+                      pars['tolerance_constant'], 

+                      pars['methodTV'],

+                      pars['nonneg'],

+                      pars['printingOut'],'gpu')

+

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+

+        rms = rmse(Im, fgp_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = FGP_TV

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        

+        print ("--------Compare the results--------")

+        tolerance = 1e-05

+        diff_im = np.zeros(np.shape(fgp_cpu))

+        diff_im = abs(fgp_cpu - fgp_gpu)

+        diff_im[diff_im > tolerance] = 1

+

+        self.assertLessEqual(diff_im.sum() , 1)

+

+    def test_SB_TV_CPU_vs_GPU(self):

+        print(__name__)

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("____________SB-TV bench___________________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        

+        # set parameters

+        pars = {'algorithm' : SB_TV, \

+                'input' : u0,\

+                'regularisation_parameter':0.04, \

+                'number_of_iterations' :150 ,\

+                'tolerance_constant':1e-05,\

+                'methodTV': 0 ,\

+                'printingOut': 0 

+                }

+                

+        print ("#############SB-TV CPU####################")

+        start_time = timeit.default_timer()

+        sb_cpu = SB_TV(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['number_of_iterations'],

+                      pars['tolerance_constant'], 

+                      pars['methodTV'],

+                      pars['printingOut'],'cpu')  

+                     

+                     

+        rms = rmse(Im, sb_cpu)

+        pars['rmse'] = rms

+        

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        

+        print ("##############SB TV GPU##################")

+        start_time = timeit.default_timer()

+        try:

+            

+            sb_gpu = SB_TV(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['number_of_iterations'],

+                      pars['tolerance_constant'], 

+                      pars['methodTV'],

+                      pars['printingOut'],'gpu')

+

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+

+        rms = rmse(Im, sb_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = SB_TV

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("--------Compare the results--------")

+        tolerance = 1e-05

+        diff_im = np.zeros(np.shape(sb_cpu))

+        diff_im = abs(sb_cpu - sb_gpu)

+        diff_im[diff_im > tolerance] = 1

+        self.assertLessEqual(diff_im.sum(), 1)

+

+    def test_TGV_CPU_vs_GPU(self):

+        print(__name__)

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("____________TGV bench___________________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        

+        # set parameters

+        pars = {'algorithm' : TGV, \

+                'input' : u0,\

+                'regularisation_parameter':0.04, \

+                'alpha1':1.0,\

+                'alpha0':2.0,\

+                'number_of_iterations' :250 ,\

+                'LipshitzConstant' :12 ,\

+                }

+                

+        print ("#############TGV CPU####################")

+        start_time = timeit.default_timer()

+        tgv_cpu = TGV(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['alpha1'],

+                      pars['alpha0'],

+                      pars['number_of_iterations'],

+                      pars['LipshitzConstant'],'cpu')

+                     

+        rms = rmse(Im, tgv_cpu)

+        pars['rmse'] = rms

+        

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        

+        print ("##############TGV GPU##################")

+        start_time = timeit.default_timer()

+        try:

+            tgv_gpu = TGV(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['alpha1'],

+                      pars['alpha0'],

+                      pars['number_of_iterations'],

+                      pars['LipshitzConstant'],'gpu')

+

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+

+        rms = rmse(Im, tgv_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = TGV

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("--------Compare the results--------")

+        tolerance = 1e-05

+        diff_im = np.zeros(np.shape(tgv_gpu))

+        diff_im = abs(tgv_cpu - tgv_gpu)

+        diff_im[diff_im > tolerance] = 1

+        self.assertLessEqual(diff_im.sum() , 1)

+

+    def test_LLT_ROF_CPU_vs_GPU(self):

+        print(__name__)

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("____________LLT-ROF bench___________________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        

+        # set parameters

+        pars = {'algorithm' : LLT_ROF, \

+                'input' : u0,\

+                'regularisation_parameterROF':0.04, \

+                'regularisation_parameterLLT':0.01, \

+                'number_of_iterations' :1000 ,\

+                'time_marching_parameter' :0.0001 ,\

+                }

+                

+        print ("#############LLT- ROF CPU####################")

+        start_time = timeit.default_timer()

+        lltrof_cpu = LLT_ROF(pars['input'], 

+                      pars['regularisation_parameterROF'],

+                      pars['regularisation_parameterLLT'],

+                      pars['number_of_iterations'],

+                      pars['time_marching_parameter'],'cpu')

+        

+        rms = rmse(Im, lltrof_cpu)

+        pars['rmse'] = rms

+        

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("#############LLT- ROF GPU####################")

+        start_time = timeit.default_timer()

+        try:

+            lltrof_gpu = LLT_ROF(pars['input'], 

+                      pars['regularisation_parameterROF'],

+                      pars['regularisation_parameterLLT'],

+                      pars['number_of_iterations'],

+                      pars['time_marching_parameter'],'gpu')

+        

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+

+        rms = rmse(Im, lltrof_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = LLT_ROF

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("--------Compare the results--------")

+        tolerance = 1e-04

+        diff_im = np.zeros(np.shape(lltrof_gpu))

+        diff_im = abs(lltrof_cpu - lltrof_gpu)

+        diff_im[diff_im > tolerance] = 1

+        self.assertLessEqual(diff_im.sum(), 1)

+

+    def test_NDF_CPU_vs_GPU(self):

+        print(__name__)

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("_______________NDF bench___________________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        

+        # set parameters

+        pars = {'algorithm' : NDF, \

+                'input' : u0,\

+                'regularisation_parameter':0.06, \

+                'edge_parameter':0.04,\

+                'number_of_iterations' :1000 ,\

+                'time_marching_parameter':0.025,\

+                'penalty_type':  1

+                }

+                

+        print ("#############NDF CPU####################")

+        start_time = timeit.default_timer()

+        ndf_cpu = NDF(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['edge_parameter'], 

+                      pars['number_of_iterations'],

+                      pars['time_marching_parameter'], 

+                      pars['penalty_type'],'cpu')

+                     

+        rms = rmse(Im, ndf_cpu)

+        pars['rmse'] = rms

+        

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        

+        print ("##############NDF GPU##################")

+        start_time = timeit.default_timer()

+        try:

+            ndf_gpu = NDF(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['edge_parameter'], 

+                      pars['number_of_iterations'],

+                      pars['time_marching_parameter'], 

+                      pars['penalty_type'],'gpu')

+                     

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+        rms = rmse(Im, ndf_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = NDF

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("--------Compare the results--------")

+        tolerance = 1e-05

+        diff_im = np.zeros(np.shape(ndf_cpu))

+        diff_im = abs(ndf_cpu - ndf_gpu)

+        diff_im[diff_im > tolerance] = 1

+        self.assertLessEqual(diff_im.sum(), 1)

+

+        

+    def test_Diff4th_CPU_vs_GPU(self):

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("___Anisotropic Diffusion 4th Order (2D)____")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        # set parameters

+        pars = {'algorithm' : Diff4th, \

+        'input' : u0,\

+        'regularisation_parameter':3.5, \

+        'edge_parameter':0.02,\

+        'number_of_iterations' :500 ,\

+        'time_marching_parameter':0.001

+        }

+        

+        print ("#############Diff4th CPU####################")

+        start_time = timeit.default_timer()

+        diff4th_cpu = Diff4th(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['edge_parameter'], 

+                      pars['number_of_iterations'],

+                      pars['time_marching_parameter'],'cpu')

+                     

+        rms = rmse(Im, diff4th_cpu)

+        pars['rmse'] = rms

+

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("##############Diff4th GPU##################")

+        start_time = timeit.default_timer()

+        try:

+            diff4th_gpu = Diff4th(pars['input'], 

+                      pars['regularisation_parameter'],

+                      pars['edge_parameter'], 

+                      pars['number_of_iterations'],

+                      pars['time_marching_parameter'], 'gpu')

+                     

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+        rms = rmse(Im, diff4th_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = Diff4th

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("--------Compare the results--------")

+        tolerance = 1e-05

+        diff_im = np.zeros(np.shape(diff4th_cpu))

+        diff_im = abs(diff4th_cpu - diff4th_gpu)

+        diff_im[diff_im > tolerance] = 1

+        self.assertLessEqual(diff_im.sum() , 1)

+

+    def test_FDGdTV_CPU_vs_GPU(self):

+        filename = os.path.join("lena_gray_512.tif")

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        

+        Im = Im/255

+        perc = 0.05

+        u0 = Im + np.random.normal(loc = 0 ,

+                                          scale = perc * Im , 

+                                          size = np.shape(Im))

+        u_ref = Im + np.random.normal(loc = 0 ,

+                                          scale = 0.01 * Im , 

+                                          size = np.shape(Im))

+        

+        # map the u0 u0->u0>0

+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)

+        u0 = u0.astype('float32')

+        u_ref = u_ref.astype('float32')

+        

+

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("____________FGP-dTV bench___________________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        

+        # set parameters

+        pars = {'algorithm' : FGP_dTV, \

+                'input' : u0,\

+                'refdata' : u_ref,\

+                'regularisation_parameter':0.04, \

+                'number_of_iterations' :1000 ,\

+                'tolerance_constant':1e-07,\

+                'eta_const':0.2,\

+                'methodTV': 0 ,\

+                'nonneg': 0 ,\

+                'printingOut': 0 

+                }

+                

+        print ("#############FGP dTV CPU####################")

+        start_time = timeit.default_timer()

+        fgp_dtv_cpu = FGP_dTV(pars['input'], 

+                      pars['refdata'], 

+                      pars['regularisation_parameter'],

+                      pars['number_of_iterations'],

+                      pars['tolerance_constant'], 

+                      pars['eta_const'], 

+                      pars['methodTV'],

+                      pars['nonneg'],

+                      pars['printingOut'],'cpu')

+                     

+                     

+        rms = rmse(Im, fgp_dtv_cpu)

+        pars['rmse'] = rms

+        

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("##############FGP dTV GPU##################")

+        start_time = timeit.default_timer()

+        try:

+            fgp_dtv_gpu = FGP_dTV(pars['input'], 

+                      pars['refdata'], 

+                      pars['regularisation_parameter'],

+                      pars['number_of_iterations'],

+                      pars['tolerance_constant'], 

+                      pars['eta_const'], 

+                      pars['methodTV'],

+                      pars['nonneg'],

+                      pars['printingOut'],'gpu')

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+        rms = rmse(Im, fgp_dtv_gpu)

+        pars['rmse'] = rms

+        pars['algorithm'] = FGP_dTV

+        txtstr = printParametersToString(pars)

+        txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)

+        print (txtstr)

+        print ("--------Compare the results--------")

+        tolerance = 1e-05

+        diff_im = np.zeros(np.shape(fgp_dtv_cpu))

+        diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)

+        diff_im[diff_im > tolerance] = 1

+        self.assertLessEqual(diff_im.sum(), 1)

+

+    def test_cpu_ROF_TV(self):

+        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")

+        

+        filename = os.path.join("lena_gray_512.tif")

+

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        Im = Im/255

+        

+        """

+        # read noiseless image

+        Im = plt.imread(filename)

+        Im = np.asarray(Im, dtype='float32')

+        """

+        tolerance = 1e-05

+        rms_rof_exp = 8.313131464999238e-05 #expected value for ROF model

+

+        # set parameters for ROF-TV

+        pars_rof_tv = {'algorithm': ROF_TV, \

+                            'input' : Im,\

+                            'regularisation_parameter':0.04,\

+                            'number_of_iterations': 50,\

+                            'time_marching_parameter': 0.00001

+                            }

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("_________testing ROF-TV (2D, CPU)__________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        rof_cpu = ROF_TV(pars_rof_tv['input'],

+             pars_rof_tv['regularisation_parameter'],

+             pars_rof_tv['number_of_iterations'],

+             pars_rof_tv['time_marching_parameter'],'cpu')

+        rms_rof = rmse(Im, rof_cpu)

+        

+        # now compare obtained rms with the expected value

+        self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance)

+    def test_cpu_FGP_TV(self):

+        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")

+        

+        filename = os.path.join("lena_gray_512.tif")

+

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        Im = Im/255

+        """

+        # read noiseless image

+        Im = plt.imread(filename)

+        Im = np.asarray(Im, dtype='float32')

+        """

+        tolerance = 1e-05

+        rms_fgp_exp = 0.019152347 #expected value for FGP model

+        

+        pars_fgp_tv = {'algorithm' : FGP_TV, \

+                            'input' : Im,\

+                            'regularisation_parameter':0.04, \

+                            'number_of_iterations' :50 ,\

+                            'tolerance_constant':1e-06,\

+                            'methodTV': 0 ,\

+                            'nonneg': 0 ,\

+                            'printingOut': 0 

+                            }

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("_________testing FGP-TV (2D, CPU)__________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        fgp_cpu = FGP_TV(pars_fgp_tv['input'], 

+              pars_fgp_tv['regularisation_parameter'],

+              pars_fgp_tv['number_of_iterations'],

+              pars_fgp_tv['tolerance_constant'], 

+              pars_fgp_tv['methodTV'],

+              pars_fgp_tv['nonneg'],

+              pars_fgp_tv['printingOut'],'cpu')  

+        rms_fgp = rmse(Im, fgp_cpu)

+        # now compare obtained rms with the expected value

+        self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance)

+

+    def test_gpu_ROF(self):

+        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")

+        filename = os.path.join("lena_gray_512.tif")

+

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)

+        Im = np.asarray(Im, dtype='float32')

+        Im = Im/255

+        

+        tolerance = 1e-05

+        rms_rof_exp = 8.313131464999238e-05 #expected value for ROF model

+        

+        # set parameters for ROF-TV

+        pars_rof_tv = {'algorithm': ROF_TV, \

+                            'input' : Im,\

+                            'regularisation_parameter':0.04,\

+                            'number_of_iterations': 50,\

+                            'time_marching_parameter': 0.00001

+                            }

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("_________testing ROF-TV (2D, GPU)__________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        try:

+            rof_gpu = ROF_TV(pars_rof_tv['input'],

+             pars_rof_tv['regularisation_parameter'],

+             pars_rof_tv['number_of_iterations'],

+             pars_rof_tv['time_marching_parameter'],'gpu')

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+

+        rms_rof = rmse(Im, rof_gpu)

+        # now compare obtained rms with the expected value

+        self.assertLess(abs(rms_rof-rms_rof_exp) , tolerance)

+    

+    def test_gpu_FGP(self):

+        #filename = os.path.join(".." , ".." , ".." , "data" ,"testLena.npy")

+        filename = os.path.join("lena_gray_512.tif")

+

+        plt = TiffReader()

+        # read image

+        Im = plt.imread(filename)                     

+        Im = np.asarray(Im, dtype='float32')

+        Im = Im/255

+        tolerance = 1e-05

+        

+        rms_fgp_exp = 0.019152347 #expected value for FGP model

+        

+        # set parameters for FGP-TV

+        pars_fgp_tv = {'algorithm' : FGP_TV, \

+                            'input' : Im,\

+                            'regularisation_parameter':0.04, \

+                            'number_of_iterations' :50 ,\

+                            'tolerance_constant':1e-06,\

+                            'methodTV': 0 ,\

+                            'nonneg': 0 ,\

+                            'printingOut': 0 

+                            }

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        print ("_________testing FGP-TV (2D, GPU)__________")

+        print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")

+        try:

+            fgp_gpu = FGP_TV(pars_fgp_tv['input'], 

+              pars_fgp_tv['regularisation_parameter'],

+              pars_fgp_tv['number_of_iterations'],

+              pars_fgp_tv['tolerance_constant'], 

+              pars_fgp_tv['methodTV'],

+              pars_fgp_tv['nonneg'],

+              pars_fgp_tv['printingOut'],'gpu')  

+        except ValueError as ve:

+            self.skipTest("Results not comparable. GPU computing error.")

+        rms_fgp = rmse(Im, fgp_gpu)

+        # now compare obtained rms with the expected value

+

+        self.assertLess(abs(rms_fgp-rms_fgp_exp) , tolerance)

+

+if __name__ == '__main__':

+    unittest.main()

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
new file mode 100644
index 0000000..bdcb8f4
--- /dev/null
+++ b/src/CMakeLists.txt
@@ -0,0 +1,19 @@
+#   Copyright 2017 Edoardo Pasca
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+if (BUILD_MATLAB_WRAPPER)
+    add_subdirectory(Matlab)
+endif()
+if (BUILD_PYTHON_WRAPPER)
+    add_subdirectory(Python)
+endif()
\ No newline at end of file
diff --git a/src/Core/CCPiDefines.h b/src/Core/CCPiDefines.h
new file mode 100644
index 0000000..d3038f9
--- /dev/null
+++ b/src/Core/CCPiDefines.h
@@ -0,0 +1,35 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Srikanth Nagella, Edoardo Pasca, Daniil Kazantsev
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+#ifndef CCPIDEFINES_H
+#define CCPIDEFINES_H
+
+#if defined(_WIN32) || defined(__WIN32__)
+  #if defined(CCPiCore_EXPORTS) || defined(CCPiNexusWidget_EXPORTS) || defined(ContourTreeSegmentation_EXPORTS) || defined(ContourTree_EXPORTS)// add by CMake 
+    #define  CCPI_EXPORT __declspec(dllexport)
+    #define EXPIMP_TEMPLATE
+  #else
+    #define  CCPI_EXPORT __declspec(dllimport)
+    #define EXPIMP_TEMPLATE extern
+  #endif /* CCPi_EXPORTS */
+#elif defined(linux) || defined(__linux) || defined(__APPLE__)
+ #define CCPI_EXPORT
+#endif
+
+#endif
diff --git a/src/Core/CMakeLists.txt b/src/Core/CMakeLists.txt
new file mode 100644
index 0000000..b3c0dfb
--- /dev/null
+++ b/src/Core/CMakeLists.txt
@@ -0,0 +1,151 @@
+#   Copyright 2018 Edoardo Pasca
+#cmake_minimum_required (VERSION 3.0)
+
+project(RGL_core)
+#https://stackoverflow.com/questions/13298504/using-cmake-with-setup-py
+
+# The version number.
+
+set (CIL_VERSION $ENV{CIL_VERSION} CACHE INTERNAL "Core Imaging Library version" FORCE)
+
+# conda orchestrated build
+message("CIL_VERSION ${CIL_VERSION}")
+#include (GenerateExportHeader)
+
+
+find_package(OpenMP)
+if (OPENMP_FOUND)
+    set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
+    set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
+    set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_EXE_LINKER_FLAGS} ${OpenMP_CXX_FLAGS}")
+   set (CMAKE_SHARED_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_SHARED_LINKER_FLAGS} ${OpenMP_CXX_FLAGS}")
+   set (CMAKE_STATIC_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} ${OpenMP_STATIC_LINKER_FLAGS} ${OpenMP_CXX_FLAGS}")
+   
+endif()
+
+## Build the regularisers package as a library
+message("Creating Regularisers as a shared library")
+
+message("CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS}")
+message("CMAKE_C_FLAGS ${CMAKE_C_FLAGS}")
+message("CMAKE_EXE_LINKER_FLAGS ${CMAKE_EXE_LINKER_FLAGS}")
+message("CMAKE_SHARED_LINKER_FLAGS ${CMAKE_SHARED_LINKER_FLAGS}")
+message("CMAKE_STATIC_LINKER_FLAGS ${CMAKE_STATIC_LINKER_FLAGS}")
+
+set(CMAKE_BUILD_TYPE "Release")
+
+if(WIN32)
+  set (FLAGS "/DWIN32 /EHsc /DCCPiCore_EXPORTS /openmp")
+  set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${FLAGS}")
+  set (CMAKE_C_FLAGS "${CMAKE_CXX_FLAGS} ${FLAGS}")
+  set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /NODEFAULTLIB:MSVCRT.lib")
+  
+  set (EXTRA_LIBRARIES)
+		
+  message("library lib: ${LIBRARY_LIB}")
+  
+elseif(UNIX)
+   set (FLAGS "-O2 -funsigned-char -Wall  -Wl,--no-undefined  -DCCPiReconstructionIterative_EXPORTS ")  
+   set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${FLAGS}")
+   set (CMAKE_C_FLAGS "${CMAKE_CXX_FLAGS} ${FLAGS}")
+  
+   set (EXTRA_LIBRARIES 
+		"gomp"
+		"m"
+		)
+   
+endif()
+message("CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS}")
+
+## Build the regularisers package as a library
+message("Adding regularisers as a shared library")
+
+#set(CMAKE_C_COMPILER /apps/pgi/linux86-64/17.4/bin/pgcc)
+#set(CMAKE_C_FLAGS "-acc -Minfo -ta=tesla:cc20 -openmp")
+#set(CMAKE_C_FLAGS "-acc -Minfo -ta=multicore -openmp -fPIC")
+add_library(cilreg SHARED
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/FGP_TV_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/SB_TV_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/TGV_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffusion_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Diffus4th_order_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/LLT_ROF_core.c
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/ROF_TV_core.c
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/FGP_dTV_core.c
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/TNV_core.c
+            ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/Nonlocal_TV_core.c
+            ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/PatchSelect_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/utils.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/inpainters_CPU/Diffusion_Inpaint_core.c
+	    ${CMAKE_CURRENT_SOURCE_DIR}/inpainters_CPU/NonlocalMarching_Inpaint_core.c
+	    )
+target_link_libraries(cilreg ${EXTRA_LIBRARIES} )
+include_directories(cilreg PUBLIC 
+                      ${LIBRARY_INC}/include 
+					  ${CMAKE_CURRENT_SOURCE_DIR}
+		              ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_CPU/
+		              ${CMAKE_CURRENT_SOURCE_DIR}/inpainters_CPU/  )
+
+## Install
+
+if (UNIX)
+message ("I'd install into ${CMAKE_INSTALL_PREFIX}/lib")
+install(TARGETS cilreg
+	LIBRARY DESTINATION lib
+	CONFIGURATIONS ${CMAKE_BUILD_TYPE} 
+	)
+elseif(WIN32)
+message ("I'd install into ${CMAKE_INSTALL_PREFIX} lib bin")
+  install(TARGETS cilreg 
+	RUNTIME DESTINATION bin
+	ARCHIVE DESTINATION lib
+	CONFIGURATIONS ${CMAKE_BUILD_TYPE} 
+	)
+endif()
+
+
+
+# GPU Regularisers
+if (BUILD_CUDA)
+    find_package(CUDA)
+    if (CUDA_FOUND)
+      set(CUDA_NVCC_FLAGS "-Xcompiler -fPIC -shared -D_FORCE_INLINES")
+      message("CUDA FLAGS ${CUDA_NVCC_FLAGS}")
+      CUDA_ADD_LIBRARY(cilregcuda SHARED
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_ROF_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_FGP_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TV_SB_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/LLT_ROF_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/TGV_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/dTV_FGP_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/NonlDiff_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/Diffus_4thO_GPU_core.cu
+        ${CMAKE_CURRENT_SOURCE_DIR}/regularisers_GPU/PatchSelect_GPU_core.cu
+      )
+      if (UNIX)
+        message ("I'd install into ${CMAKE_INSTALL_PREFIX}/lib")
+        install(TARGETS cilregcuda
+        LIBRARY DESTINATION lib
+        CONFIGURATIONS ${CMAKE_BUILD_TYPE} 
+        )
+      elseif(WIN32)
+        message ("I'd install into ${CMAKE_INSTALL_PREFIX} lib bin")
+        install(TARGETS cilregcuda
+        RUNTIME DESTINATION bin
+        ARCHIVE DESTINATION lib
+        CONFIGURATIONS ${CMAKE_BUILD_TYPE} 
+        )
+      endif()
+    else()
+      message("CUDA NOT FOUND")
+    endif()
+endif()
+
+if (${BUILD_MATLAB_WRAPPER})
+  if (WIN32)
+        install(TARGETS cilreg DESTINATION ${MATLAB_DEST})
+        if (CUDA_FOUND)
+            install(TARGETS cilregcuda DESTINATION ${MATLAB_DEST})
+        endif()
+  endif()
+endif()
diff --git a/src/Core/inpainters_CPU/Diffusion_Inpaint_core.c b/src/Core/inpainters_CPU/Diffusion_Inpaint_core.c
new file mode 100644
index 0000000..08b168a
--- /dev/null
+++ b/src/Core/inpainters_CPU/Diffusion_Inpaint_core.c
@@ -0,0 +1,322 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Diffusion_Inpaint_core.h"
+#include "utils.h"
+
+/*sign function*/
+int signNDF_inc(float x) {
+    return (x > 0) - (x < 0);
+}
+
+/* C-OMP implementation of linear and nonlinear diffusion [1,2] for inpainting task (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Image/volume to inpaint
+ * 2. Mask of the same size as (1) in 'unsigned char' format  (ones mark the region to inpaint, zeros belong to the data)
+ * 3. lambda - regularization parameter
+ * 4. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 5. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 6. tau - time-marching step for explicit scheme
+ * 7. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight
+ *
+ * Output:
+ * [1] Inpainted image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+ */
+
+float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ)
+{
+    long i, pointsone;
+    float sigmaPar2;
+    sigmaPar2 = sigmaPar/sqrt(2.0f);
+    
+    /* copy into output */
+    copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ));
+    
+    pointsone = 0;
+    for (i=0; i<dimY*dimX*dimZ; i++) if (Mask[i] == 1) pointsone++;
+        
+    if (pointsone == 0) printf("%s \n", "Nothing to inpaint, zero mask!");
+    else {
+    
+    if (dimZ == 1) {
+    /* running 2D diffusion iterations */
+    for(i=0; i < iterationsNumb; i++) {
+            if (sigmaPar == 0.0f) LinearDiff_Inp_2D(Input, Mask, Output, lambdaPar, tau, (long)(dimX), (long)(dimY)); /* linear diffusion (heat equation) */
+            else NonLinearDiff_Inp_2D(Input, Mask, Output, lambdaPar, sigmaPar2, tau, penaltytype, (long)(dimX), (long)(dimY)); /* nonlinear diffusion */
+		}
+	}
+	else {
+	/* running 3D diffusion iterations */
+    for(i=0; i < iterationsNumb; i++) {
+            if (sigmaPar == 0.0f) LinearDiff_Inp_3D(Input, Mask, Output, lambdaPar, tau, (long)(dimX), (long)(dimY), (long)(dimZ));
+            else NonLinearDiff_Inp_3D(Input, Mask, Output, lambdaPar, sigmaPar2, tau, penaltytype, (long)(dimX), (long)(dimY), (long)(dimZ));
+            }
+         }
+	}
+    return *Output;
+}
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+/* linear diffusion (heat equation) */
+float LinearDiff_Inp_2D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float tau, long dimX, long dimY)
+{
+	long i,j,i1,i2,j1,j2,index;
+	float e,w,n,s,e1,w1,n1,s1;
+	
+#pragma omp parallel for shared(Input,Mask) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1)
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = j*dimX+i;
+             
+             if (Mask[index] > 0) {
+				/*inpainting process*/
+                e = Output[j*dimX+i1];
+                w = Output[j*dimX+i2];
+                n = Output[j1*dimX+i];
+                s = Output[j2*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index]));
+			}
+		}}
+	return *Output;
+}
+
+/* nonlinear diffusion */
+float NonLinearDiff_Inp_2D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY)
+{
+	long i,j,i1,i2,j1,j2,index;
+	float e,w,n,s,e1,w1,n1,s1;
+	
+#pragma omp parallel for shared(Input,Mask) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1)
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = j*dimX+i;
+                
+        if (Mask[index] > 0) {
+		/*inpainting process*/
+                e = Output[j*dimX+i1];
+                w = Output[j*dimX+i2];
+                n = Output[j1*dimX+i];
+                s = Output[j2*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+
+            if (penaltytype == 1){
+            /* Huber penalty */
+            if (fabs(e1) > sigmaPar) e1 =  signNDF_inc(e1);
+            else e1 = e1/sigmaPar;
+            
+            if (fabs(w1) > sigmaPar) w1 =  signNDF_inc(w1);
+            else w1 = w1/sigmaPar;
+            
+            if (fabs(n1) > sigmaPar) n1 =  signNDF_inc(n1);
+            else n1 = n1/sigmaPar;
+            
+            if (fabs(s1) > sigmaPar) s1 =  signNDF_inc(s1);
+            else s1 = s1/sigmaPar;
+            }
+            else if (penaltytype == 2) {
+            /* Perona-Malik */
+            e1 = (e1)/(1.0f + powf((e1/sigmaPar),2));
+            w1 = (w1)/(1.0f + powf((w1/sigmaPar),2));
+            n1 = (n1)/(1.0f + powf((n1/sigmaPar),2));
+            s1 = (s1)/(1.0f + powf((s1/sigmaPar),2));
+            }
+            else if (penaltytype == 3) {
+            /* Tukey Biweight */
+            if (fabs(e1) <= sigmaPar) e1 =  e1*powf((1.0f - powf((e1/sigmaPar),2)), 2);
+            else e1 = 0.0f;
+            if (fabs(w1) <= sigmaPar) w1 =  w1*powf((1.0f - powf((w1/sigmaPar),2)), 2);
+            else w1 = 0.0f;
+            if (fabs(n1) <= sigmaPar) n1 =  n1*powf((1.0f - powf((n1/sigmaPar),2)), 2);
+            else n1 = 0.0f;
+            if (fabs(s1) <= sigmaPar) s1 =  s1*powf((1.0f - powf((s1/sigmaPar),2)), 2);
+            else s1 = 0.0f;
+            }
+            else {
+				printf("%s \n", "No penalty function selected! Use 1,2 or 3.");
+				break;
+				}
+           Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index]));  
+		}
+		}}
+	return *Output;
+}
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+/* linear diffusion (heat equation) */
+float LinearDiff_Inp_3D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float tau, long dimX, long dimY, long dimZ)
+{
+	long i,j,k,i1,i2,j1,j2,k1,k2,index;
+	float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1;
+	
+#pragma omp parallel for shared(Input,Mask) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1,k,k1,k2,u1,d1,u,d)
+for(k=0; k<dimZ; k++) {
+	k1 = k+1; if (k1 == dimZ) k1 = k-1;
+    k2 = k-1; if (k2 < 0) k2 = k+1;
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = (dimX*dimY)*k + j*dimX+i;
+            
+            if (Mask[index] > 0) {
+			/*inpainting process*/
+            
+                e = Output[(dimX*dimY)*k + j*dimX+i1];
+                w = Output[(dimX*dimY)*k + j*dimX+i2];
+                n = Output[(dimX*dimY)*k + j1*dimX+i];
+                s = Output[(dimX*dimY)*k + j2*dimX+i];
+                u = Output[(dimX*dimY)*k1 + j*dimX+i];
+                d = Output[(dimX*dimY)*k2 + j*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                u1 = u - Output[index];
+                d1 = d - Output[index];
+                
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index]));  
+			}
+		}}}
+	return *Output;
+}
+
+float NonLinearDiff_Inp_3D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY, long dimZ)
+{
+	long i,j,k,i1,i2,j1,j2,k1,k2,index;
+	float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1;
+	
+#pragma omp parallel for shared(Input,Mask) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1,k,k1,k2,u1,d1,u,d)
+for(k=0; k<dimZ; k++) {
+	k1 = k+1; if (k1 == dimZ) k1 = k-1;
+    k2 = k-1; if (k2 < 0) k2 = k+1;
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = (dimX*dimY)*k + j*dimX+i;
+            
+        if (Mask[index] > 0) {
+			/*inpainting process*/
+                e = Output[(dimX*dimY)*k + j*dimX+i1];
+                w = Output[(dimX*dimY)*k + j*dimX+i2];
+                n = Output[(dimX*dimY)*k + j1*dimX+i];
+                s = Output[(dimX*dimY)*k + j2*dimX+i];
+                u = Output[(dimX*dimY)*k1 + j*dimX+i];
+                d = Output[(dimX*dimY)*k2 + j*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                u1 = u - Output[index];
+                d1 = d - Output[index];
+                
+             if (penaltytype == 1){
+            /* Huber penalty */
+            if (fabs(e1) > sigmaPar) e1 =  signNDF_inc(e1);
+            else e1 = e1/sigmaPar;
+            
+            if (fabs(w1) > sigmaPar) w1 =  signNDF_inc(w1);
+            else w1 = w1/sigmaPar;
+            
+            if (fabs(n1) > sigmaPar) n1 =  signNDF_inc(n1);
+            else n1 = n1/sigmaPar;
+            
+            if (fabs(s1) > sigmaPar) s1 =  signNDF_inc(s1);
+            else s1 = s1/sigmaPar;
+            
+            if (fabs(u1) > sigmaPar) u1 =  signNDF_inc(u1);
+            else u1 = u1/sigmaPar;
+            
+            if (fabs(d1) > sigmaPar) d1 =  signNDF_inc(d1);
+            else d1 = d1/sigmaPar;
+            }
+            else if (penaltytype == 2) {
+            /* Perona-Malik */
+            e1 = (e1)/(1.0f + powf((e1/sigmaPar),2));
+            w1 = (w1)/(1.0f + powf((w1/sigmaPar),2));
+            n1 = (n1)/(1.0f + powf((n1/sigmaPar),2));
+            s1 = (s1)/(1.0f + powf((s1/sigmaPar),2));
+            u1 = (u1)/(1.0f + powf((u1/sigmaPar),2));
+            d1 = (d1)/(1.0f + powf((d1/sigmaPar),2));
+            }
+            else if (penaltytype == 3) {
+            /* Tukey Biweight */
+            if (fabs(e1) <= sigmaPar) e1 =  e1*powf((1.0f - powf((e1/sigmaPar),2)), 2);
+            else e1 = 0.0f;
+            if (fabs(w1) <= sigmaPar) w1 =  w1*powf((1.0f - powf((w1/sigmaPar),2)), 2);
+            else w1 = 0.0f;
+            if (fabs(n1) <= sigmaPar) n1 =  n1*powf((1.0f - powf((n1/sigmaPar),2)), 2);
+            else n1 = 0.0f;
+            if (fabs(s1) <= sigmaPar) s1 =  s1*powf((1.0f - powf((s1/sigmaPar),2)), 2);
+            else s1 = 0.0f;
+            if (fabs(u1) <= sigmaPar) u1 =  u1*powf((1.0f - powf((u1/sigmaPar),2)), 2);
+            else u1 = 0.0f;
+            if (fabs(d1) <= sigmaPar) d1 =  d1*powf((1.0f - powf((d1/sigmaPar),2)), 2);
+            else d1 = 0.0f;
+            }
+            else {
+				printf("%s \n", "No penalty function selected! Use 1,2 or 3.");
+				break;
+				}
+
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index]));  
+		}
+		}}}
+	return *Output;
+}
diff --git a/src/Core/inpainters_CPU/Diffusion_Inpaint_core.h b/src/Core/inpainters_CPU/Diffusion_Inpaint_core.h
new file mode 100644
index 0000000..a96fe79
--- /dev/null
+++ b/src/Core/inpainters_CPU/Diffusion_Inpaint_core.h
@@ -0,0 +1,61 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+
+/* C-OMP implementation of linear and nonlinear diffusion [1,2] for inpainting task (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Image/volume to inpaint
+ * 2. Mask of the same size as (1) in 'unsigned char' format  (ones mark the region to inpaint, zeros belong to the data)
+ * 3. lambda - regularization parameter
+ * 4. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 5. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 6. tau - time-marching step for explicit scheme
+ * 7. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight
+ *
+ * Output:
+ * [1] Inpainted image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+ */
+
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb,  float tau, int penaltytype, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float LinearDiff_Inp_2D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float tau, long dimX, long dimY);
+CCPI_EXPORT float NonLinearDiff_Inp_2D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY);
+CCPI_EXPORT float LinearDiff_Inp_3D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float tau, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float NonLinearDiff_Inp_3D(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c b/src/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c
new file mode 100644
index 0000000..b488ca4
--- /dev/null
+++ b/src/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c
@@ -0,0 +1,188 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "NonlocalMarching_Inpaint_core.h"
+#include "utils.h"
+
+
+/* C-OMP implementation of Nonlocal Vertical Marching inpainting method (2D case)
+ * The method is heuristic but computationally efficent (especially for larger images).
+ * It developed specifically to smoothly inpaint horizontal or inclined missing data regions in sinograms
+ * The method WILL not work satisfactory if you have lengthy vertical stripes of missing data
+ *
+ * Input:
+ * 1. 2D image or sinogram with horizontal or inclined regions of missing data
+ * 2. Mask of the same size as A in 'unsigned char' format  (ones mark the region to inpaint, zeros belong to the data)
+ * 3. Linear increment to increase searching window size in iterations, values from 1-3 is a good choice
+ *
+ * Output:
+ * 1. Inpainted image or a sinogram
+ * 2. updated mask
+ *
+ * Reference: D. Kazantsev (paper in preparation)
+ */
+
+float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int trigger, int dimX, int dimY, int dimZ)
+{
+    int i, j, i_m, j_m, counter, iter, iterations_number, W_fullsize, switchmask, switchcurr, counterElements;
+    float *Gauss_weights;
+    
+    /* copying M to M_upd */
+    copyIm_unchar(M, M_upd, dimX, dimY, 1);
+    
+    /* Copying the image */
+    copyIm(Input, Output, dimX, dimY, 1);
+    
+    /* Find how many inpainting iterations (equal to the number of ones) required based on a mask  */
+    if (iterationsNumb == 0) {
+        iterations_number = 0;
+        for (i=0; i<dimY*dimX; i++) {
+            if (M[i] == 1) iterations_number++;
+        }
+        if ((int)(iterations_number/dimY) > dimX) iterations_number = dimX;
+    }
+    else iterations_number = iterationsNumb;
+    
+    if (iterations_number == 0) printf("%s \n", "Nothing to inpaint, zero mask!");
+    else {
+        
+        printf("%s %i \n", "Max iteration number equals to:", iterations_number);
+        
+        /* Inpainting iterations run here*/
+        int W_halfsize = 1;
+        for(iter=0; iter < iterations_number; iter++) {
+            
+            //if (mod (iter, 2) == 0) {W_halfsize += 1;}
+            // printf("%i \n", W_halfsize);
+            
+            /* pre-calculation of Gaussian distance weights  */
+            W_fullsize = (int)(2*W_halfsize + 1); /*full size of similarity window */
+            Gauss_weights = (float*)calloc(W_fullsize*W_fullsize,sizeof(float ));
+            counter = 0;
+            for(i_m=-W_halfsize; i_m<=W_halfsize; i_m++) {
+                for(j_m=-W_halfsize; j_m<=W_halfsize; j_m++) {
+                    Gauss_weights[counter] = exp(-(pow((i_m), 2) + pow((j_m), 2))/(2*W_halfsize*W_halfsize));
+                    counter++;
+                }
+            }
+            
+            if (trigger == 0) {
+                /*Matlab*/
+#pragma omp parallel for shared(Output, M_upd, Gauss_weights) private(i, j, switchmask, switchcurr)
+                for(j=0; j<dimY; j++) {
+                    switchmask = 0;
+                    for(i=0; i<dimX; i++) {
+                        switchcurr = 0;
+                        if ((M_upd[j*dimX + i] == 1) && (switchmask == 0)) {
+                            /* perform inpainting of the current pixel */
+                            inpaint_func(Output, M_upd, Gauss_weights, i, j, dimX, dimY, W_halfsize, W_fullsize);
+                            /* add value to the mask*/
+                            M_upd[j*dimX + i] = 0;
+                            switchmask = 1; switchcurr = 1;
+                        }
+                        if ((M_upd[j*dimX + i] == 0) && (switchmask == 1) && (switchcurr == 0)) {
+                            /* perform inpainting of the previous (i-1) pixel */
+                            inpaint_func(Output, M_upd, Gauss_weights, i-1, j, dimX, dimY, W_halfsize, W_fullsize);
+                            /* add value to the mask*/
+                            M_upd[(j)*dimX + i-1] = 0;
+                            switchmask = 0;
+                        }
+                    }
+                }
+            }
+            else {
+                /*Python*/
+                /* find a point in the mask to inpaint */
+#pragma omp parallel for shared(Output, M_upd, Gauss_weights) private(i, j, switchmask, switchcurr)
+                for(i=0; i<dimX; i++) {
+                    switchmask = 0;
+                    for(j=0; j<dimY; j++) {
+                        switchcurr = 0;
+                        if ((M_upd[j*dimX + i] == 1) && (switchmask == 0)) {
+                            /* perform inpainting of the current pixel */
+                            inpaint_func(Output, M_upd, Gauss_weights, i, j, dimX, dimY, W_halfsize, W_fullsize);
+                            /* add value to the mask*/
+                            M_upd[j*dimX + i] = 0;
+                            switchmask = 1; switchcurr = 1;
+                        }
+                        if ((M_upd[j*dimX + i] == 0) && (switchmask == 1) && (switchcurr == 0)) {
+                            /* perform inpainting of the previous (j-1) pixel */
+                            inpaint_func(Output, M_upd, Gauss_weights, i, j-1, dimX, dimY, W_halfsize, W_fullsize);
+                            /* add value to the mask*/
+                            M_upd[(j-1)*dimX + i] = 0;
+                            switchmask = 0;
+                        }
+                    }
+                }
+            }
+            free(Gauss_weights);
+            
+            /* check if possible to terminate iterations earlier */
+            counterElements = 0;
+            for(i=0; i<dimX*dimY; i++) if (M_upd[i] == 0) counterElements++;
+            
+            if (counterElements == dimX*dimY) {
+                printf("%s \n", "Padding completed!");
+                break;
+            }
+            W_halfsize += SW_increment;
+        }
+        printf("%s %i \n", "Iterations stopped at:", iter);
+    }
+    return *Output;
+}
+
+float inpaint_func(float *U, unsigned char *M_upd, float *Gauss_weights, int i, int j, int dimX, int dimY, int W_halfsize, int W_fullsize)
+{
+    int i1, j1, i_m, j_m, counter;
+    float sum_val, sumweight;
+    
+    /*method 1: inpainting based on Euclidian weights */
+    sumweight = 0.0f;
+    counter = 0; sum_val = 0.0f;
+    for(i_m=-W_halfsize; i_m<=W_halfsize; i_m++) {
+        i1 = i+i_m;
+        for(j_m=-W_halfsize; j_m<=W_halfsize; j_m++) {
+            j1 = j+j_m;
+            if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) {
+                if (M_upd[j1*dimX + i1] == 0) {
+                    sumweight += Gauss_weights[counter];
+                }
+            }
+            counter++;
+        }
+    }
+    counter = 0; sum_val = 0.0f;
+    for(i_m=-W_halfsize; i_m<=W_halfsize; i_m++) {
+        i1 = i+i_m;
+        for(j_m=-W_halfsize; j_m<=W_halfsize; j_m++) {
+            j1 = j+j_m;
+            if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) {
+                if ((M_upd[j1*dimX + i1] == 0) && (sumweight != 0.0f)) {
+                    /* we have data so add it with Euc weight */
+                    sum_val += (Gauss_weights[counter]/sumweight)*U[j1*dimX + i1];
+                }
+            }
+            counter++;
+        }
+    }
+    U[j*dimX + i] = sum_val;
+    return *U;
+}
+
diff --git a/src/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.h b/src/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.h
new file mode 100644
index 0000000..0f99ed4
--- /dev/null
+++ b/src/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.h
@@ -0,0 +1,54 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+
+/* C-OMP implementation of Nonlocal Vertical Marching inpainting method (2D case)
+ * The method is heuristic but computationally efficent (especially for larger images).
+ * It developed specifically to smoothly inpaint horizontal or inclined missing data regions in sinograms
+ * The method WILL not work satisfactory if you have lengthy vertical stripes of missing data
+ *
+ * Inputs:
+ * 1. 2D image or sinogram with horizontal or inclined regions of missing data
+ * 2. Mask of the same size as A in 'unsigned char' format  (ones mark the region to inpaint, zeros belong to the data)
+ * 3. Linear increment to increase searching window size in iterations, values from 1-3 is a good choice
+
+ * Output:
+ * 1. Inpainted image or a sinogram
+ * 2. updated mask
+ *
+ * Reference: TBA
+ */
+
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int trigger, int dimX, int dimY, int dimZ);
+CCPI_EXPORT float inpaint_func(float *U, unsigned char *M_upd, float *Gauss_weights, int i, int j, int dimX, int dimY, int W_halfsize, int W_fullsize);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/Diffus4th_order_core.c b/src/Core/regularisers_CPU/Diffus4th_order_core.c
new file mode 100644
index 0000000..01f4f64
--- /dev/null
+++ b/src/Core/regularisers_CPU/Diffus4th_order_core.c
@@ -0,0 +1,250 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Diffus4th_order_core.h"
+#include "utils.h"
+
+#define EPS 1.0e-7
+
+/* C-OMP implementation of fourth-order diffusion scheme [1] for piecewise-smooth recovery (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma)
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 5. tau - time-marching step for the explicit scheme
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.
+ */
+
+float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
+{
+    int i,DimTotal;
+    float sigmaPar2;
+    float *W_Lapl=NULL;
+    sigmaPar2 = sigmaPar*sigmaPar;
+    DimTotal =  dimX*dimY*dimZ;
+    
+    W_Lapl = calloc(DimTotal, sizeof(float));
+    
+    /* copy into output */
+    copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ));
+    
+    if (dimZ == 1) {
+    /* running 2D diffusion iterations */
+    for(i=0; i < iterationsNumb; i++) {
+            /* Calculating weighted Laplacian */
+            Weighted_Laplc2D(W_Lapl, Output, sigmaPar2, dimX, dimY);
+            /* Perform iteration step */
+            Diffusion_update_step2D(Output, Input, W_Lapl, lambdaPar, sigmaPar2, tau, (long)(dimX), (long)(dimY));
+		}
+	}
+	else {
+	/* running 3D diffusion iterations */
+    for(i=0; i < iterationsNumb; i++) {
+		    /* Calculating weighted Laplacian */
+            Weighted_Laplc3D(W_Lapl, Output, sigmaPar2, dimX, dimY, dimZ);
+            /* Perform iteration step */
+            Diffusion_update_step3D(Output, Input, W_Lapl, lambdaPar, sigmaPar2, tau, (long)(dimX), (long)(dimY), (long)(dimZ));
+		}
+	}
+	free(W_Lapl);
+    return *Output;
+}
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+float Weighted_Laplc2D(float *W_Lapl, float *U0, float sigma, long dimX, long dimY)
+{   
+    long i,j,i1,i2,j1,j2,index;
+    float gradX, gradX_sq, gradY, gradY_sq, gradXX, gradYY, gradXY, xy_2, denom, V_norm, V_orth, c, c_sq;
+
+        #pragma omp parallel for shared(W_Lapl) private(i,j,i1,i2,j1,j2,index,gradX, gradX_sq, gradY, gradY_sq, gradXX, gradYY, gradXY, xy_2, denom, V_norm, V_orth, c, c_sq)
+        for(i=0; i<dimX; i++) {
+			 /* symmetric boundary conditions */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            for(j=0; j<dimY; j++) {
+				 /* symmetric boundary conditions */
+				j1 = j+1; if (j1 == dimY) j1 = j-1;
+				j2 = j-1; if (j2 < 0) j2 = j+1;
+				
+				index = j*dimX+i;
+				
+				gradX = 0.5f*(U0[j*dimX+i2] - U0[j*dimX+i1]);
+				gradX_sq = pow(gradX,2);
+				
+				gradY = 0.5f*(U0[j2*dimX+i] - U0[j1*dimX+i]);
+                gradY_sq = pow(gradY,2);
+                
+                gradXX = U0[j*dimX+i2] + U0[j*dimX+i1] - 2*U0[index];
+                gradYY = U0[j2*dimX+i] + U0[j1*dimX+i] - 2*U0[index];
+                
+                gradXY = 0.25f*(U0[j2*dimX+i2] + U0[j1*dimX+i1] - U0[j1*dimX+i2] - U0[j2*dimX+i1]);
+                xy_2 = 2.0f*gradX*gradY*gradXY;
+                
+                denom =  gradX_sq + gradY_sq;
+                
+                if (denom <= EPS) {
+                    V_norm = (gradXX*gradX_sq + xy_2 + gradYY*gradY_sq)/EPS;
+                    V_orth = (gradXX*gradY_sq - xy_2 + gradYY*gradX_sq)/EPS; 
+                    }
+                else  {
+                    V_norm = (gradXX*gradX_sq + xy_2 + gradYY*gradY_sq)/denom;
+                    V_orth = (gradXX*gradY_sq - xy_2 + gradYY*gradX_sq)/denom;  
+                    }
+
+                c = 1.0f/(1.0f + denom/sigma);
+                c_sq = c*c;
+                
+                W_Lapl[index] = c_sq*V_norm + c*V_orth;
+            }
+        }
+        return *W_Lapl;
+}
+
+float Diffusion_update_step2D(float *Output, float *Input, float *W_Lapl, float lambdaPar, float sigmaPar2, float tau, long dimX, long dimY)
+{
+	long i,j,i1,i2,j1,j2,index;
+    float gradXXc, gradYYc;
+
+            #pragma omp parallel for shared(Output, Input, W_Lapl) private(i,j,i1,i2,j1,j2,index,gradXXc,gradYYc)
+        for(i=0; i<dimX; i++) {
+			 /* symmetric boundary conditions */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            for(j=0; j<dimY; j++) {
+				 /* symmetric boundary conditions */
+				j1 = j+1; if (j1 == dimY) j1 = j-1;
+				j2 = j-1; if (j2 < 0) j2 = j+1;
+					index = j*dimX+i;
+					
+                    gradXXc = W_Lapl[j*dimX+i2] + W_Lapl[j*dimX+i1] - 2*W_Lapl[index];
+                    gradYYc = W_Lapl[j2*dimX+i] + W_Lapl[j1*dimX+i] - 2*W_Lapl[index];
+
+                    Output[index] += tau*(-lambdaPar*(gradXXc + gradYYc) - (Output[index] - Input[index]));
+                }
+            }
+	return *Output;
+}
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+float Weighted_Laplc3D(float *W_Lapl, float *U0, float sigma, long dimX, long dimY, long dimZ)
+{   
+    long i,j,k,i1,i2,j1,j2,k1,k2,index;
+    float gradX, gradX_sq, gradY, gradY_sq, gradXX, gradYY, gradXY, xy_2, denom, V_norm, V_orth, c, c_sq, gradZ, gradZ_sq, gradZZ, gradXZ, gradYZ, xyz_1, xyz_2;
+        
+        #pragma omp parallel for shared(W_Lapl) private(i,j,k,i1,i2,j1,j2,k1,k2,index,gradX, gradX_sq, gradY, gradY_sq, gradXX, gradYY, gradXY, xy_2, denom, V_norm, V_orth, c, c_sq, gradZ, gradZ_sq, gradZZ, gradXZ, gradYZ, xyz_1, xyz_2)
+        for(i=0; i<dimX; i++) {
+			 /* symmetric boundary conditions */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            for(j=0; j<dimY; j++) {
+				/* symmetric boundary conditions */
+				j1 = j+1; if (j1 == dimY) j1 = j-1;
+				j2 = j-1; if (j2 < 0) j2 = j+1;
+				
+				for(k=0; k<dimZ; k++) {
+				/* symmetric boundary conditions */
+				k1 = k+1; if (k1 == dimZ) k1 = k-1;
+				k2 = k-1; if (k2 < 0) k2 = k+1;
+				
+				index = (dimX*dimY)*k + j*dimX+i;
+				
+				gradX = 0.5f*(U0[(dimX*dimY)*k + j*dimX+i2] - U0[(dimX*dimY)*k + j*dimX+i1]);
+				gradX_sq = pow(gradX,2);
+				
+				gradY = 0.5f*(U0[(dimX*dimY)*k + j2*dimX+i] - U0[(dimX*dimY)*k + j1*dimX+i]);
+                gradY_sq = pow(gradY,2);
+                
+                gradZ = 0.5f*(U0[(dimX*dimY)*k2 + j*dimX+i] - U0[(dimX*dimY)*k1 + j*dimX+i]);
+                gradZ_sq = pow(gradZ,2);
+                
+                gradXX = U0[(dimX*dimY)*k + j*dimX+i2] + U0[(dimX*dimY)*k + j*dimX+i1] - 2*U0[index];
+                gradYY = U0[(dimX*dimY)*k + j2*dimX+i] + U0[(dimX*dimY)*k + j1*dimX+i] - 2*U0[index];
+                gradZZ = U0[(dimX*dimY)*k2 + j*dimX+i] + U0[(dimX*dimY)*k1 + j*dimX+i] - 2*U0[index];
+                                
+                gradXY = 0.25f*(U0[(dimX*dimY)*k + j2*dimX+i2] + U0[(dimX*dimY)*k + j1*dimX+i1] - U0[(dimX*dimY)*k + j1*dimX+i2] - U0[(dimX*dimY)*k + j2*dimX+i1]);
+                gradXZ = 0.25f*(U0[(dimX*dimY)*k2 + j*dimX+i2] - U0[(dimX*dimY)*k2+j*dimX+i1] - U0[(dimX*dimY)*k1+j*dimX+i2] + U0[(dimX*dimY)*k1+j*dimX+i1]);
+                gradYZ = 0.25f*(U0[(dimX*dimY)*k2 +j2*dimX+i] - U0[(dimX*dimY)*k2+j1*dimX+i] - U0[(dimX*dimY)*k1+j2*dimX+i] + U0[(dimX*dimY)*k1+j1*dimX+i]);
+                
+                xy_2  = 2.0f*gradX*gradY*gradXY;
+                xyz_1 = 2.0f*gradX*gradZ*gradXZ;
+                xyz_2 = 2.0f*gradY*gradZ*gradYZ;
+                
+                denom =  gradX_sq + gradY_sq + gradZ_sq;
+                
+					if (denom <= EPS) {
+					V_norm = (gradXX*gradX_sq + gradYY*gradY_sq + gradZZ*gradZ_sq + xy_2 + xyz_1 + xyz_2)/EPS;
+                    V_orth = ((gradY_sq + gradZ_sq)*gradXX + (gradX_sq + gradZ_sq)*gradYY + (gradX_sq + gradY_sq)*gradZZ - xy_2 - xyz_1 - xyz_2)/EPS;
+					}
+					else  {
+					V_norm = (gradXX*gradX_sq + gradYY*gradY_sq + gradZZ*gradZ_sq + xy_2 + xyz_1 + xyz_2)/denom;
+                    V_orth = ((gradY_sq + gradZ_sq)*gradXX + (gradX_sq + gradZ_sq)*gradYY + (gradX_sq + gradY_sq)*gradZZ - xy_2 - xyz_1 - xyz_2)/denom;
+					}
+
+                c = 1.0f/(1.0f + denom/sigma);
+                c_sq = c*c;
+                
+                W_Lapl[index] = c_sq*V_norm + c*V_orth;
+				}
+            }
+        }
+        return *W_Lapl;
+}
+
+float Diffusion_update_step3D(float *Output, float *Input, float *W_Lapl, float lambdaPar, float sigmaPar2, float tau, long dimX, long dimY, long dimZ)
+{
+	long i,j,i1,i2,j1,j2,index,k,k1,k2;
+    float gradXXc, gradYYc, gradZZc;
+
+        #pragma omp parallel for shared(Output, Input, W_Lapl) private(i,j,i1,i2,j1,j2,k,k1,k2,index,gradXXc,gradYYc,gradZZc)
+        for(i=0; i<dimX; i++) {
+			 /* symmetric boundary conditions */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            for(j=0; j<dimY; j++) {
+				/* symmetric boundary conditions */
+				j1 = j+1; if (j1 == dimY) j1 = j-1;
+				j2 = j-1; if (j2 < 0) j2 = j+1;
+				
+				for(k=0; k<dimZ; k++) {
+				/* symmetric boundary conditions */
+				k1 = k+1; if (k1 == dimZ) k1 = k-1;
+				k2 = k-1; if (k2 < 0) k2 = k+1;
+				
+				index = (dimX*dimY)*k + j*dimX+i;
+				
+                    gradXXc = W_Lapl[(dimX*dimY)*k + j*dimX+i2] + W_Lapl[(dimX*dimY)*k + j*dimX+i1] - 2*W_Lapl[index];
+                    gradYYc = W_Lapl[(dimX*dimY)*k + j2*dimX+i] + W_Lapl[(dimX*dimY)*k + j1*dimX+i] - 2*W_Lapl[index];
+                    gradZZc = W_Lapl[(dimX*dimY)*k2 + j*dimX+i] + W_Lapl[(dimX*dimY)*k1 + j*dimX+i] - 2*W_Lapl[index];
+                    
+                    Output[index] += tau*(-lambdaPar*(gradXXc + gradYYc + gradZZc) - (Output[index] - Input[index]));
+                }
+            }
+		}
+	return *Output;
+}
diff --git a/src/Core/regularisers_CPU/Diffus4th_order_core.h b/src/Core/regularisers_CPU/Diffus4th_order_core.h
new file mode 100644
index 0000000..d81afcb
--- /dev/null
+++ b/src/Core/regularisers_CPU/Diffus4th_order_core.h
@@ -0,0 +1,55 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+/* C-OMP implementation of fourth-order diffusion scheme [1] for piecewise-smooth recovery (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma)
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 5. tau - time-marching step for explicit scheme
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.
+ */
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
+CCPI_EXPORT float Weighted_Laplc2D(float *W_Lapl, float *U0, float sigma, long dimX, long dimY);
+CCPI_EXPORT float Diffusion_update_step2D(float *Output, float *Input, float *W_Lapl, float lambdaPar, float sigmaPar2, float tau, long dimX, long dimY);
+CCPI_EXPORT float Weighted_Laplc3D(float *W_Lapl, float *U0, float sigma, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float Diffusion_update_step3D(float *Output, float *Input, float *W_Lapl, float lambdaPar, float sigmaPar2, float tau, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/Diffusion_core.c b/src/Core/regularisers_CPU/Diffusion_core.c
new file mode 100644
index 0000000..b765796
--- /dev/null
+++ b/src/Core/regularisers_CPU/Diffusion_core.c
@@ -0,0 +1,307 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Diffusion_core.h"
+#include "utils.h"
+
+#define EPS 1.0e-5
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+/*sign function*/
+int signNDFc(float x) {
+    return (x > 0) - (x < 0);
+}
+
+/* C-OMP implementation of linear and nonlinear diffusion with the regularisation model [1,2] (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 5. tau - time-marching step for explicit scheme
+ * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+ */
+
+float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ)
+{
+    int i;
+    float sigmaPar2;
+    sigmaPar2 = sigmaPar/sqrt(2.0f);
+    
+    /* copy into output */
+    copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ));
+    
+    if (dimZ == 1) {
+    /* running 2D diffusion iterations */
+    for(i=0; i < iterationsNumb; i++) {
+            if (sigmaPar == 0.0f) LinearDiff2D(Input, Output, lambdaPar, tau, (long)(dimX), (long)(dimY)); /* linear diffusion (heat equation) */
+            else NonLinearDiff2D(Input, Output, lambdaPar, sigmaPar2, tau, penaltytype, (long)(dimX), (long)(dimY)); /* nonlinear diffusion */
+		}
+	}
+	else {
+	/* running 3D diffusion iterations */
+    for(i=0; i < iterationsNumb; i++) {
+            if (sigmaPar == 0.0f) LinearDiff3D(Input, Output, lambdaPar, tau, (long)(dimX), (long)(dimY), (long)(dimZ));
+            else NonLinearDiff3D(Input, Output, lambdaPar, sigmaPar2, tau, penaltytype, (long)(dimX), (long)(dimY), (long)(dimZ));
+		}
+	}
+    return *Output;
+}
+
+
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+/* linear diffusion (heat equation) */
+float LinearDiff2D(float *Input, float *Output, float lambdaPar, float tau, long dimX, long dimY)
+{
+	long i,j,i1,i2,j1,j2,index;
+	float e,w,n,s,e1,w1,n1,s1;
+	
+#pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1)
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = j*dimX+i;
+            
+                e = Output[j*dimX+i1];
+                w = Output[j*dimX+i2];
+                n = Output[j1*dimX+i];
+                s = Output[j2*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index]));  
+		}}
+	return *Output;
+}
+
+/* nonlinear diffusion */
+float NonLinearDiff2D(float *Input, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY)
+{
+	long i,j,i1,i2,j1,j2,index;
+	float e,w,n,s,e1,w1,n1,s1;
+	
+#pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1)
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = j*dimX+i;
+            
+                e = Output[j*dimX+i1];
+                w = Output[j*dimX+i2];
+                n = Output[j1*dimX+i];
+                s = Output[j2*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                
+            if (penaltytype == 1){
+            /* Huber penalty */
+            if (fabs(e1) > sigmaPar) e1 =  signNDFc(e1);
+            else e1 = e1/sigmaPar;
+            
+            if (fabs(w1) > sigmaPar) w1 =  signNDFc(w1);
+            else w1 = w1/sigmaPar;
+            
+            if (fabs(n1) > sigmaPar) n1 =  signNDFc(n1);
+            else n1 = n1/sigmaPar;
+            
+            if (fabs(s1) > sigmaPar) s1 =  signNDFc(s1);
+            else s1 = s1/sigmaPar;
+            }
+            else if (penaltytype == 2) {
+            /* Perona-Malik */
+            e1 = (e1)/(1.0f + powf((e1/sigmaPar),2));
+            w1 = (w1)/(1.0f + powf((w1/sigmaPar),2));
+            n1 = (n1)/(1.0f + powf((n1/sigmaPar),2));
+            s1 = (s1)/(1.0f + powf((s1/sigmaPar),2));
+            }
+            else if (penaltytype == 3) {
+            /* Tukey Biweight */
+            if (fabs(e1) <= sigmaPar) e1 =  e1*powf((1.0f - powf((e1/sigmaPar),2)), 2);
+            else e1 = 0.0f;
+            if (fabs(w1) <= sigmaPar) w1 =  w1*powf((1.0f - powf((w1/sigmaPar),2)), 2);
+            else w1 = 0.0f;
+            if (fabs(n1) <= sigmaPar) n1 =  n1*powf((1.0f - powf((n1/sigmaPar),2)), 2);
+            else n1 = 0.0f;
+            if (fabs(s1) <= sigmaPar) s1 =  s1*powf((1.0f - powf((s1/sigmaPar),2)), 2);
+            else s1 = 0.0f;
+            }
+            else {
+				printf("%s \n", "No penalty function selected! Use 1,2 or 3.");
+				break;
+				}
+           Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index]));  
+		}}
+	return *Output;
+}
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+/* linear diffusion (heat equation) */
+float LinearDiff3D(float *Input, float *Output, float lambdaPar, float tau, long dimX, long dimY, long dimZ)
+{
+	long i,j,k,i1,i2,j1,j2,k1,k2,index;
+	float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1;
+	
+#pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1,k,k1,k2,u1,d1,u,d)
+for(k=0; k<dimZ; k++) {
+	k1 = k+1; if (k1 == dimZ) k1 = k-1;
+    k2 = k-1; if (k2 < 0) k2 = k+1;
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = (dimX*dimY)*k + j*dimX+i;
+            
+                e = Output[(dimX*dimY)*k + j*dimX+i1];
+                w = Output[(dimX*dimY)*k + j*dimX+i2];
+                n = Output[(dimX*dimY)*k + j1*dimX+i];
+                s = Output[(dimX*dimY)*k + j2*dimX+i];
+                u = Output[(dimX*dimY)*k1 + j*dimX+i];
+                d = Output[(dimX*dimY)*k2 + j*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                u1 = u - Output[index];
+                d1 = d - Output[index];
+                
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index]));  
+		}}}
+	return *Output;
+}
+
+float NonLinearDiff3D(float *Input, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY, long dimZ)
+{
+	long i,j,k,i1,i2,j1,j2,k1,k2,index;
+	float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1;
+	
+#pragma omp parallel for shared(Input) private(index,i,j,i1,i2,j1,j2,e,w,n,s,e1,w1,n1,s1,k,k1,k2,u1,d1,u,d)
+for(k=0; k<dimZ; k++) {
+	k1 = k+1; if (k1 == dimZ) k1 = k-1;
+    k2 = k-1; if (k2 < 0) k2 = k+1;
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = (dimX*dimY)*k + j*dimX+i;
+            
+                e = Output[(dimX*dimY)*k + j*dimX+i1];
+                w = Output[(dimX*dimY)*k + j*dimX+i2];
+                n = Output[(dimX*dimY)*k + j1*dimX+i];
+                s = Output[(dimX*dimY)*k + j2*dimX+i];
+                u = Output[(dimX*dimY)*k1 + j*dimX+i];
+                d = Output[(dimX*dimY)*k2 + j*dimX+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                u1 = u - Output[index];
+                d1 = d - Output[index];
+                
+             if (penaltytype == 1){
+            /* Huber penalty */
+            if (fabs(e1) > sigmaPar) e1 =  signNDFc(e1);
+            else e1 = e1/sigmaPar;
+            
+            if (fabs(w1) > sigmaPar) w1 =  signNDFc(w1);
+            else w1 = w1/sigmaPar;
+            
+            if (fabs(n1) > sigmaPar) n1 =  signNDFc(n1);
+            else n1 = n1/sigmaPar;
+            
+            if (fabs(s1) > sigmaPar) s1 =  signNDFc(s1);
+            else s1 = s1/sigmaPar;
+            
+            if (fabs(u1) > sigmaPar) u1 =  signNDFc(u1);
+            else u1 = u1/sigmaPar;
+            
+            if (fabs(d1) > sigmaPar) d1 =  signNDFc(d1);
+            else d1 = d1/sigmaPar;            
+            }
+            else if (penaltytype == 2) {
+            /* Perona-Malik */
+            e1 = (e1)/(1.0f + powf((e1/sigmaPar),2));
+            w1 = (w1)/(1.0f + powf((w1/sigmaPar),2));
+            n1 = (n1)/(1.0f + powf((n1/sigmaPar),2));
+            s1 = (s1)/(1.0f + powf((s1/sigmaPar),2));
+            u1 = (u1)/(1.0f + powf((u1/sigmaPar),2));
+            d1 = (d1)/(1.0f + powf((d1/sigmaPar),2));
+            }
+            else if (penaltytype == 3) {
+            /* Tukey Biweight */
+            if (fabs(e1) <= sigmaPar) e1 =  e1*powf((1.0f - powf((e1/sigmaPar),2)), 2);
+            else e1 = 0.0f;
+            if (fabs(w1) <= sigmaPar) w1 =  w1*powf((1.0f - powf((w1/sigmaPar),2)), 2);
+            else w1 = 0.0f;
+            if (fabs(n1) <= sigmaPar) n1 =  n1*powf((1.0f - powf((n1/sigmaPar),2)), 2);
+            else n1 = 0.0f;
+            if (fabs(s1) <= sigmaPar) s1 =  s1*powf((1.0f - powf((s1/sigmaPar),2)), 2);
+            else s1 = 0.0f;
+            if (fabs(u1) <= sigmaPar) u1 =  u1*powf((1.0f - powf((u1/sigmaPar),2)), 2);
+            else u1 = 0.0f;
+            if (fabs(d1) <= sigmaPar) d1 =  d1*powf((1.0f - powf((d1/sigmaPar),2)), 2);
+            else d1 = 0.0f;
+            }
+            else {
+				printf("%s \n", "No penalty function selected! Use 1,2 or 3.");
+				break;
+				}
+
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index]));  
+		}}}
+	return *Output;
+}
diff --git a/src/Core/regularisers_CPU/Diffusion_core.h b/src/Core/regularisers_CPU/Diffusion_core.h
new file mode 100644
index 0000000..cc36dad
--- /dev/null
+++ b/src/Core/regularisers_CPU/Diffusion_core.h
@@ -0,0 +1,59 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+
+/* C-OMP implementation of linear and nonlinear diffusion with the regularisation model [1,2] (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 5. tau - time-marching step for explicit scheme
+ * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+ */
+
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb,  float tau, int penaltytype, int dimX, int dimY, int dimZ);
+CCPI_EXPORT float LinearDiff2D(float *Input, float *Output, float lambdaPar, float tau, long dimX, long dimY);
+CCPI_EXPORT float NonLinearDiff2D(float *Input, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY);
+CCPI_EXPORT float LinearDiff3D(float *Input, float *Output, float lambdaPar, float tau, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float NonLinearDiff3D(float *Input, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/FGP_TV_core.c b/src/Core/regularisers_CPU/FGP_TV_core.c
new file mode 100644
index 0000000..68d58b7
--- /dev/null
+++ b/src/Core/regularisers_CPU/FGP_TV_core.c
@@ -0,0 +1,321 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "FGP_TV_core.h"
+
+/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambdaPar - regularization parameter 
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant 
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default) 
+ * 7. print information: 0 (off) or 1 (on) 
+ *
+ * Output:
+ * [1] Filtered/regularized image
+ *
+ * This function is based on the Matlab's code and paper by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ */
+ 
+float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ)
+{
+	int ll;
+    long j, DimTotal;
+	float re, re1;
+	float tk = 1.0f;
+    float tkp1=1.0f;
+    int count = 0;
+	
+	if (dimZ <= 1) {
+		/*2D case */
+		float *Output_prev=NULL, *P1=NULL, *P2=NULL, *P1_prev=NULL, *P2_prev=NULL, *R1=NULL, *R2=NULL;
+		DimTotal = (long)(dimX*dimY);
+		
+        Output_prev = calloc(DimTotal, sizeof(float));
+        P1 = calloc(DimTotal, sizeof(float));
+        P2 = calloc(DimTotal, sizeof(float));
+        P1_prev = calloc(DimTotal, sizeof(float));
+        P2_prev = calloc(DimTotal, sizeof(float));
+        R1 = calloc(DimTotal, sizeof(float));
+        R2 = calloc(DimTotal, sizeof(float)); 
+		
+		/* begin iterations */
+        for(ll=0; ll<iterationsNumb; ll++) {
+            
+            /* computing the gradient of the objective function */
+            Obj_func2D(Input, Output, R1, R2, lambdaPar, (long)(dimX), (long)(dimY));
+            
+            /* apply nonnegativity */
+            if (nonneg == 1) for(j=0; j<DimTotal; j++) {if (Output[j] < 0.0f) Output[j] = 0.0f;}
+            
+            /*Taking a step towards minus of the gradient*/
+            Grad_func2D(P1, P2, Output, R1, R2, lambdaPar, (long)(dimX), (long)(dimY));
+            
+            /* projection step */
+            Proj_func2D(P1, P2, methodTV, DimTotal);
+            
+            /*updating R and t*/
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            Rupd_func2D(P1, P1_prev, P2, P2_prev, R1, R2, tkp1, tk, DimTotal);
+            
+            /* check early stopping criteria */
+            re = 0.0f; re1 = 0.0f;
+            for(j=0; j<DimTotal; j++)
+            {
+                re += pow(Output[j] - Output_prev[j],2);
+                re1 += pow(Output[j],2);
+            }
+            re = sqrt(re)/sqrt(re1);
+            if (re < epsil)  count++;
+				if (count > 4) break;
+            
+            /*storing old values*/
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), 1l);
+            copyIm(P1, P1_prev, (long)(dimX), (long)(dimY), 1l);
+            copyIm(P2, P2_prev, (long)(dimX), (long)(dimY), 1l);
+            tk = tkp1;
+        }
+        if (printM == 1) printf("FGP-TV iterations stopped at iteration %i \n", ll);   
+		free(Output_prev); free(P1); free(P2); free(P1_prev); free(P2_prev); free(R1); free(R2);		
+	}
+	else {
+		/*3D case*/
+		float *Output_prev=NULL, *P1=NULL, *P2=NULL, *P3=NULL, *P1_prev=NULL, *P2_prev=NULL, *P3_prev=NULL, *R1=NULL, *R2=NULL, *R3=NULL;		
+		DimTotal = (long)(dimX*dimY*dimZ);        
+        
+        Output_prev = calloc(DimTotal, sizeof(float));
+        P1 = calloc(DimTotal, sizeof(float));
+        P2 = calloc(DimTotal, sizeof(float));
+        P3 = calloc(DimTotal, sizeof(float));
+        P1_prev = calloc(DimTotal, sizeof(float));
+        P2_prev = calloc(DimTotal, sizeof(float));        
+        P3_prev = calloc(DimTotal, sizeof(float));        
+        R1 = calloc(DimTotal, sizeof(float));
+        R2 = calloc(DimTotal, sizeof(float)); 
+        R3 = calloc(DimTotal, sizeof(float)); 
+		
+		    /* begin iterations */
+        for(ll=0; ll<iterationsNumb; ll++) {
+            
+            /* computing the gradient of the objective function */
+            Obj_func3D(Input, Output, R1, R2, R3, lambdaPar, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /* apply nonnegativity */
+            if (nonneg == 1) for(j=0; j<DimTotal; j++) {if (Output[j] < 0.0f) Output[j] = 0.0f;}  
+            
+            /*Taking a step towards minus of the gradient*/
+            Grad_func3D(P1, P2, P3, Output, R1, R2, R3, lambdaPar, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /* projection step */
+            Proj_func3D(P1, P2, P3, methodTV, DimTotal);
+            
+            /*updating R and t*/
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            Rupd_func3D(P1, P1_prev, P2, P2_prev, P3, P3_prev, R1, R2, R3, tkp1, tk, DimTotal);
+            
+            /* calculate norm - stopping rules*/
+            re = 0.0f; re1 = 0.0f;
+            for(j=0; j<DimTotal; j++)
+            {
+                re += pow(Output[j] - Output_prev[j],2);
+                re1 += pow(Output[j],2);
+            }
+            re = sqrt(re)/sqrt(re1);
+            /* stop if the norm residual is less than the tolerance EPS */
+            if (re < epsil)  count++;
+            if (count > 4) break;            
+                        
+            /*storing old values*/
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            copyIm(P1, P1_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            copyIm(P2, P2_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            copyIm(P3, P3_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            tk = tkp1;            
+        }	
+		if (printM == 1) printf("FGP-TV iterations stopped at iteration %i \n", ll);   
+		free(Output_prev); free(P1); free(P2); free(P3); free(P1_prev); free(P2_prev); free(P3_prev); free(R1); free(R2); free(R3);
+	}
+	return *Output;
+}
+
+float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, long dimX, long dimY)
+{
+    float val1, val2;
+    long i,j,index;
+#pragma omp parallel for shared(A,D,R1,R2) private(index,i,j,val1,val2)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+			index = j*dimX+i;
+            /* boundary conditions  */
+            if (i == 0) {val1 = 0.0f;} else {val1 = R1[j*dimX + (i-1)];}
+            if (j == 0) {val2 = 0.0f;} else {val2 = R2[(j-1)*dimX + i];}
+            D[index] = A[index] - lambda*(R1[index] + R2[index] - val1 - val2);
+        }}
+    return *D;
+}
+float Grad_func2D(float *P1, float *P2, float *D, float *R1, float *R2, float lambda,  long dimX, long dimY)
+{
+    float val1, val2, multip;
+    long i,j,index;
+    multip = (1.0f/(8.0f*lambda));
+#pragma omp parallel for shared(P1,P2,D,R1,R2,multip) private(index,i,j,val1,val2)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+			index = j*dimX+i;
+            /* boundary conditions */
+            if (i == dimX-1) val1 = 0.0f; else val1 = D[index] - D[j*dimX + (i+1)];
+            if (j == dimY-1) val2 = 0.0f; else val2 = D[index] - D[(j+1)*dimX + i];
+            P1[index] = R1[index] + multip*val1;
+            P2[index] = R2[index] + multip*val2;
+        }}
+    return 1;
+}
+float Proj_func2D(float *P1, float *P2, int methTV, long DimTotal)
+{
+    float val1, val2, denom, sq_denom;
+    long i;
+    if (methTV == 0) {
+        /* isotropic TV*/
+#pragma omp parallel for shared(P1,P2) private(i,denom,sq_denom)
+        for(i=0; i<DimTotal; i++) {
+                denom = powf(P1[i],2) +  powf(P2[i],2);
+                if (denom > 1.0f) {
+					sq_denom = 1.0f/sqrtf(denom);
+                    P1[i] = P1[i]*sq_denom;
+                    P2[i] = P2[i]*sq_denom;
+                }
+            }
+    }
+    else {
+        /* anisotropic TV*/
+#pragma omp parallel for shared(P1,P2) private(i,val1,val2)
+        for(i=0; i<DimTotal; i++) {
+                val1 = fabs(P1[i]);
+                val2 = fabs(P2[i]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                P1[i] = P1[i]/val1;
+                P2[i] = P2[i]/val2;
+            }
+    }
+    return 1;
+}
+float Rupd_func2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, long DimTotal)
+{
+    long i;
+    float multip;
+    multip = ((tk-1.0f)/tkp1);
+#pragma omp parallel for shared(P1,P2,P1_old,P2_old,R1,R2,multip) private(i)
+    for(i=0; i<DimTotal; i++) {       
+            R1[i] = P1[i] + multip*(P1[i] - P1_old[i]);
+            R2[i] = P2[i] + multip*(P2[i] - P2_old[i]);
+        }
+    return 1;
+}
+
+/* 3D-case related Functions */
+/*****************************************************************/
+float Obj_func3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, long dimX, long dimY, long dimZ)
+{
+    float val1, val2, val3;
+    long i,j,k,index;
+#pragma omp parallel for shared(A,D,R1,R2,R3) private(index,i,j,k,val1,val2,val3)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;
+                /* boundary conditions */
+                if (i == 0) {val1 = 0.0f;} else {val1 = R1[(dimX*dimY)*k + j*dimX + (i-1)];}
+                if (j == 0) {val2 = 0.0f;} else {val2 = R2[(dimX*dimY)*k + (j-1)*dimX + i];}
+                if (k == 0) {val3 = 0.0f;} else {val3 = R3[(dimX*dimY)*(k-1) + j*dimX + i];}
+                D[index] = A[index] - lambda*(R1[index] + R2[index] + R3[index] - val1 - val2 - val3);
+            }}}
+    return *D;
+}
+float Grad_func3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float lambda, long dimX, long dimY, long dimZ)
+{
+    float val1, val2, val3, multip;
+    long i,j,k, index;
+    multip = (1.0f/(26.0f*lambda));
+#pragma omp parallel for shared(P1,P2,P3,D,R1,R2,R3,multip) private(index,i,j,k,val1,val2,val3)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;				
+                /* boundary conditions */
+                if (i == dimX-1) val1 = 0.0f; else val1 = D[index] - D[(dimX*dimY)*k + j*dimX + (i+1)];
+                if (j == dimY-1) val2 = 0.0f; else val2 = D[index] - D[(dimX*dimY)*k + (j+1)*dimX + i];
+                if (k == dimZ-1) val3 = 0.0f; else val3 = D[index] - D[(dimX*dimY)*(k+1) + j*dimX + i];
+                P1[index] = R1[index] + multip*val1;
+                P2[index] = R2[index] + multip*val2;
+                P3[index] = R3[index] + multip*val3;
+            }}}
+    return 1;
+}
+float Proj_func3D(float *P1, float *P2, float *P3, int methTV, long DimTotal)
+{		
+    float val1, val2, val3, denom, sq_denom;
+    long i;
+    if (methTV == 0) {
+	/* isotropic TV*/
+	#pragma omp parallel for shared(P1,P2,P3) private(i,val1,val2,val3,sq_denom)
+    for(i=0; i<DimTotal; i++) {        
+				denom = powf(P1[i],2) + powf(P2[i],2) + powf(P3[i],2);
+                if (denom > 1.0f) {
+					sq_denom = 1.0f/sqrtf(denom);
+                    P1[i] = P1[i]*sq_denom;
+                    P2[i] = P2[i]*sq_denom;
+                    P3[i] = P3[i]*sq_denom;
+                }
+			}
+	}    
+    else {
+    /* anisotropic TV*/
+#pragma omp parallel for shared(P1,P2,P3) private(i,val1,val2,val3)
+    for(i=0; i<DimTotal; i++) {
+                val1 = fabs(P1[i]);
+                val2 = fabs(P2[i]);
+                val3 = fabs(P3[i]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                if (val3 < 1.0f) {val3 = 1.0f;}                
+                P1[i] = P1[i]/val1;
+                P2[i] = P2[i]/val2;
+                P3[i] = P3[i]/val3;
+            }
+		}
+    return 1;
+}
+float Rupd_func3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, long DimTotal)
+{
+    long i;
+    float multip;
+    multip = ((tk-1.0f)/tkp1);
+#pragma omp parallel for shared(P1,P2,P3,P1_old,P2_old,P3_old,R1,R2,R3,multip) private(i)
+    for(i=0; i<DimTotal; i++) {
+                R1[i] = P1[i] + multip*(P1[i] - P1_old[i]);
+                R2[i] = P2[i] + multip*(P2[i] - P2_old[i]);
+                R3[i] = P3[i] + multip*(P3[i] - P3_old[i]);
+            }
+    return 1;
+}
diff --git a/src/Core/regularisers_CPU/FGP_TV_core.h b/src/Core/regularisers_CPU/FGP_TV_core.h
new file mode 100644
index 0000000..3418604
--- /dev/null
+++ b/src/Core/regularisers_CPU/FGP_TV_core.h
@@ -0,0 +1,63 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+//#include <matrix.h>
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter 
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant 
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default) 
+ * 7. print information: 0 (off) or 1 (on) 
+ *
+ * Output:
+ * [1] Filtered/regularized image
+ *
+ * This function is based on the Matlab's code and paper by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ */
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float Obj_func2D(float *A, float *D, float *R1, float *R2, float lambda, long dimX, long dimY);
+CCPI_EXPORT float Grad_func2D(float *P1, float *P2, float *D, float *R1, float *R2, float lambda, long dimX, long dimY);
+CCPI_EXPORT float Proj_func2D(float *P1, float *P2, int methTV, long DimTotal);
+CCPI_EXPORT float Rupd_func2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, long DimTotal);
+
+CCPI_EXPORT float Obj_func3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float Grad_func3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float lambda, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float Proj_func3D(float *P1, float *P2, float *P3, int methTV, long DimTotal);
+CCPI_EXPORT float Rupd_func3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, long DimTotal);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/FGP_dTV_core.c b/src/Core/regularisers_CPU/FGP_dTV_core.c
new file mode 100644
index 0000000..17b75ff
--- /dev/null
+++ b/src/Core/regularisers_CPU/FGP_dTV_core.c
@@ -0,0 +1,441 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "FGP_dTV_core.h"
+
+/* C-OMP implementation of FGP-dTV [1,2] denoising/regularization model (2D/3D case)
+ * which employs structural similarity of the level sets of two images/volumes, see [1,2]
+ * The current implementation updates image 1 while image 2 is being fixed.
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. Additional reference image/volume of the same dimensions as (1) [REQUIRED]
+ * 3. lambdaPar - regularization parameter [REQUIRED]
+ * 4. Number of iterations [OPTIONAL]
+ * 5. eplsilon: tolerance constant [OPTIONAL]
+ * 6. eta: smoothing constant to calculate gradient of the reference [OPTIONAL] * 
+ * 7. TV-type: methodTV - 'iso' (0) or 'l1' (1) [OPTIONAL]
+ * 8. nonneg: 'nonnegativity (0 is OFF by default) [OPTIONAL]
+ * 9. print information: 0 (off) or 1 (on) [OPTIONAL]
+ *
+ * Output:
+ * [1] Filtered/regularized image/volume
+ *
+ * This function is based on the Matlab's codes and papers by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ * [2] M. J. Ehrhardt and M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), pp. 1084–1106
+ */
+ 
+float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ)
+{
+	int ll;
+    long j, DimTotal;
+	float re, re1;
+	float tk = 1.0f;
+    float tkp1=1.0f;
+    int count = 0;
+	
+	if (dimZ <= 1) {
+		/*2D case */
+		float *Output_prev=NULL, *P1=NULL, *P2=NULL, *P1_prev=NULL, *P2_prev=NULL, *R1=NULL, *R2=NULL, *InputRef_x=NULL, *InputRef_y=NULL;
+		DimTotal = (long)(dimX*dimY);
+		
+        Output_prev = calloc(DimTotal, sizeof(float));
+        P1 = calloc(DimTotal, sizeof(float));
+        P2 = calloc(DimTotal, sizeof(float));
+        P1_prev = calloc(DimTotal, sizeof(float));
+        P2_prev = calloc(DimTotal, sizeof(float));
+        R1 = calloc(DimTotal, sizeof(float));
+        R2 = calloc(DimTotal, sizeof(float)); 
+        InputRef_x = calloc(DimTotal, sizeof(float)); 
+        InputRef_y = calloc(DimTotal, sizeof(float)); 
+
+		/* calculate gradient field (smoothed) for the reference image */
+		GradNorm_func2D(InputRef, InputRef_x, InputRef_y, eta, (long)(dimX), (long)(dimY));
+		
+		/* begin iterations */
+        for(ll=0; ll<iterationsNumb; ll++) {
+            
+            /*projects a 2D vector field R-1,2 onto the orthogonal complement of another 2D vector field InputRef_xy*/                    
+            ProjectVect_func2D(R1, R2, InputRef_x, InputRef_y, (long)(dimX), (long)(dimY));
+            
+            /* computing the gradient of the objective function */
+            Obj_dfunc2D(Input, Output, R1, R2, lambdaPar, (long)(dimX), (long)(dimY));
+            
+            /* apply nonnegativity */
+            if (nonneg == 1) for(j=0; j<DimTotal; j++) {if (Output[j] < 0.0f) Output[j] = 0.0f;}
+            
+            /*Taking a step towards minus of the gradient*/
+            Grad_dfunc2D(P1, P2, Output, R1, R2, InputRef_x, InputRef_y, lambdaPar, (long)(dimX), (long)(dimY));
+            
+            /* projection step */
+            Proj_dfunc2D(P1, P2, methodTV, DimTotal);
+            
+            /*updating R and t*/
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            Rupd_dfunc2D(P1, P1_prev, P2, P2_prev, R1, R2, tkp1, tk, DimTotal);
+            
+            /* check early stopping criteria */
+            re = 0.0f; re1 = 0.0f;
+            for(j=0; j<DimTotal; j++)
+            {
+                re += pow(Output[j] - Output_prev[j],2);
+                re1 += pow(Output[j],2);
+            }
+            re = sqrt(re)/sqrt(re1);
+            if (re < epsil)  count++;
+				if (count > 4) break;
+            
+            /*storing old values*/
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), 1l);
+            copyIm(P1, P1_prev, (long)(dimX), (long)(dimY), 1l);
+            copyIm(P2, P2_prev, (long)(dimX), (long)(dimY), 1l);
+            tk = tkp1;
+        }
+        if (printM == 1) printf("FGP-dTV iterations stopped at iteration %i \n", ll);   
+		free(Output_prev); free(P1); free(P2); free(P1_prev); free(P2_prev); free(R1); free(R2); free(InputRef_x); free(InputRef_y);
+	}
+	else {
+		/*3D case*/
+		float *Output_prev=NULL, *P1=NULL, *P2=NULL, *P3=NULL, *P1_prev=NULL, *P2_prev=NULL, *P3_prev=NULL, *R1=NULL, *R2=NULL, *R3=NULL, *InputRef_x=NULL, *InputRef_y=NULL, *InputRef_z=NULL; 
+		DimTotal = (long)(dimX*dimY*dimZ);
+        
+        Output_prev = calloc(DimTotal, sizeof(float));
+        P1 = calloc(DimTotal, sizeof(float));
+        P2 = calloc(DimTotal, sizeof(float));
+        P3 = calloc(DimTotal, sizeof(float));
+        P1_prev = calloc(DimTotal, sizeof(float));
+        P2_prev = calloc(DimTotal, sizeof(float));
+        P3_prev = calloc(DimTotal, sizeof(float));
+        R1 = calloc(DimTotal, sizeof(float));
+        R2 = calloc(DimTotal, sizeof(float)); 
+        R3 = calloc(DimTotal, sizeof(float)); 
+        InputRef_x = calloc(DimTotal, sizeof(float)); 
+        InputRef_y = calloc(DimTotal, sizeof(float)); 
+        InputRef_z = calloc(DimTotal, sizeof(float)); 
+
+		/* calculate gradient field (smoothed) for the reference volume */
+		GradNorm_func3D(InputRef, InputRef_x, InputRef_y, InputRef_z, eta, (long)(dimX), (long)(dimY), (long)(dimZ));
+		
+		/* begin iterations */
+        for(ll=0; ll<iterationsNumb; ll++) {
+
+			 /*projects a 3D vector field R-1,2,3 onto the orthogonal complement of another 3D vector field InputRef_xyz*/
+            ProjectVect_func3D(R1, R2, R3, InputRef_x, InputRef_y, InputRef_z, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /* computing the gradient of the objective function */
+            Obj_dfunc3D(Input, Output, R1, R2, R3, lambdaPar, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /* apply nonnegativity */
+            if (nonneg == 1) for(j=0; j<DimTotal; j++) {if (Output[j] < 0.0f) Output[j] = 0.0f;}  
+            
+            /*Taking a step towards minus of the gradient*/
+            Grad_dfunc3D(P1, P2, P3, Output, R1, R2, R3, InputRef_x, InputRef_y, InputRef_z, lambdaPar, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /* projection step */
+            Proj_dfunc3D(P1, P2, P3, methodTV, DimTotal);
+            
+            /*updating R and t*/
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            Rupd_dfunc3D(P1, P1_prev, P2, P2_prev, P3, P3_prev, R1, R2, R3, tkp1, tk, DimTotal);
+            
+            /* calculate norm - stopping rules*/
+            re = 0.0f; re1 = 0.0f;
+            for(j=0; j<DimTotal; j++)
+            {
+                re += pow(Output[j] - Output_prev[j],2);
+                re1 += pow(Output[j],2);
+            }
+            re = sqrt(re)/sqrt(re1);
+            /* stop if the norm residual is less than the tolerance EPS */
+            if (re < epsil)  count++;
+            if (count > 4) break;            
+                        
+            /*storing old values*/
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            copyIm(P1, P1_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            copyIm(P2, P2_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            copyIm(P3, P3_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            tk = tkp1;            
+        }	
+		if (printM == 1) printf("FGP-dTV iterations stopped at iteration %i \n", ll);   
+		free(Output_prev); free(P1); free(P2); free(P3); free(P1_prev); free(P2_prev); free(P3_prev); free(R1); free(R2); free(R3); free(InputRef_x); free(InputRef_y); free(InputRef_z);
+	}
+	return *Output;
+}
+
+
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+
+float GradNorm_func2D(float *B, float *B_x, float *B_y, float eta, long dimX, long dimY)
+{
+    long i,j,index;
+    float val1, val2, gradX, gradY, magn;
+#pragma omp parallel for shared(B, B_x, B_y) private(i,j,index,val1,val2,gradX,gradY,magn)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+			index = j*dimX+i;
+            /* zero boundary conditions */
+            if (i == dimX-1) {val1 = 0.0f;} else {val1 = B[j*dimX + (i+1)];}
+            if (j == dimY-1) {val2 = 0.0f;} else {val2 = B[(j+1)*dimX + i];}
+            gradX = val1 - B[index];
+            gradY = val2 - B[index];
+            magn = pow(gradX,2) + pow(gradY,2);
+            magn = sqrt(magn + pow(eta,2)); /* the eta-smoothed gradients magnitude */
+            B_x[index] = gradX/magn;
+            B_y[index] = gradY/magn;
+        }}
+    return 1;
+}
+
+float ProjectVect_func2D(float *R1, float *R2, float *B_x, float *B_y, long dimX, long dimY)
+{
+    long i,j,index;
+    float in_prod;
+#pragma omp parallel for shared(R1, R2, B_x, B_y) private(index,i,j,in_prod)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+			index = j*dimX+i;
+            in_prod = R1[index]*B_x[index] + R2[index]*B_y[index];   /* calculate inner product */
+            R1[index] = R1[index] - in_prod*B_x[index];
+            R2[index] = R2[index] - in_prod*B_y[index];
+        }}
+    return 1;
+}
+
+float Obj_dfunc2D(float *A, float *D, float *R1, float *R2, float lambda, long dimX, long dimY)
+{
+    float val1, val2;
+    long i,j,index;
+#pragma omp parallel for shared(A,D,R1,R2) private(index,i,j,val1,val2)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+			index = j*dimX+i;
+            /* boundary conditions  */
+            if (i == 0) {val1 = 0.0f;} else {val1 = R1[j*dimX + (i-1)];}
+            if (j == 0) {val2 = 0.0f;} else {val2 = R2[(j-1)*dimX + i];}
+            D[index] = A[index] - lambda*(R1[index] + R2[index] - val1 - val2);
+        }}
+    return *D;
+}
+float Grad_dfunc2D(float *P1, float *P2, float *D, float *R1, float *R2, float *B_x, float *B_y, float lambda, long dimX, long dimY)
+{
+    float val1, val2, multip, in_prod;
+    long i,j,index;
+    multip = (1.0f/(8.0f*lambda));
+#pragma omp parallel for shared(P1,P2,D,R1,R2,B_x,B_y,multip) private(i,j,index,val1,val2,in_prod)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+			index = j*dimX+i;
+            /* boundary conditions */
+            if (i == dimX-1) val1 = 0.0f; else val1 = D[index] - D[j*dimX + (i+1)];
+            if (j == dimY-1) val2 = 0.0f; else val2 = D[index] - D[(j+1)*dimX + i];
+            
+            in_prod = val1*B_x[index] + val2*B_y[index];   /* calculate inner product */
+            val1 = val1 - in_prod*B_x[index];
+            val2 = val2 - in_prod*B_y[index];
+            
+            P1[index] = R1[index] + multip*val1;
+            P2[index] = R2[index] + multip*val2;
+
+        }}
+    return 1;
+}
+float Proj_dfunc2D(float *P1, float *P2, int methTV, long DimTotal)
+{
+    float val1, val2, denom, sq_denom;
+    long i;
+    if (methTV == 0) {
+        /* isotropic TV*/
+#pragma omp parallel for shared(P1,P2) private(i,denom,sq_denom)
+        for(i=0; i<DimTotal; i++) {
+                denom = powf(P1[i],2) +  powf(P2[i],2);
+                if (denom > 1.0f) {
+					sq_denom = 1.0f/sqrtf(denom);
+                    P1[i] = P1[i]*sq_denom;
+                    P2[i] = P2[i]*sq_denom;
+                }
+            }
+    }
+    else {
+        /* anisotropic TV*/
+#pragma omp parallel for shared(P1,P2) private(i,val1,val2)
+        for(i=0; i<DimTotal; i++) {
+                val1 = fabs(P1[i]);
+                val2 = fabs(P2[i]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                P1[i] = P1[i]/val1;
+                P2[i] = P2[i]/val2;
+            }
+    }
+    return 1;
+}
+float Rupd_dfunc2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, long DimTotal)
+{
+    long i;
+    float multip;
+    multip = ((tk-1.0f)/tkp1);
+#pragma omp parallel for shared(P1,P2,P1_old,P2_old,R1,R2,multip) private(i)
+    for(i=0; i<DimTotal; i++) {       
+            R1[i] = P1[i] + multip*(P1[i] - P1_old[i]);
+            R2[i] = P2[i] + multip*(P2[i] - P2_old[i]);
+        }
+    return 1;
+}
+
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+float GradNorm_func3D(float *B, float *B_x, float *B_y, float *B_z, float eta, long dimX, long dimY, long dimZ)
+{
+    long i, j, k, index;
+    float val1, val2, val3, gradX, gradY, gradZ, magn;
+#pragma omp parallel for shared(B, B_x, B_y, B_z) private(i,j,k,index,val1,val2,val3,gradX,gradY,gradZ,magn)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+			index = (dimX*dimY)*k + j*dimX+i;
+			
+            /* zero boundary conditions */
+            if (i == dimX-1) {val1 = 0.0f;} else {val1 = B[(dimX*dimY)*k + j*dimX+(i+1)];}
+            if (j == dimY-1) {val2 = 0.0f;} else {val2 = B[(dimX*dimY)*k + (j+1)*dimX+i];}
+            if (k == dimZ-1) {val3 = 0.0f;} else {val3 = B[(dimX*dimY)*(k+1) + (j)*dimX+i];}
+            
+            gradX = val1 - B[index];
+            gradY = val2 - B[index];
+            gradZ = val3 - B[index];
+            magn = pow(gradX,2) + pow(gradY,2) + pow(gradZ,2);
+            magn = sqrt(magn + pow(eta,2)); /* the eta-smoothed gradients magnitude */
+            B_x[index] = gradX/magn;
+            B_y[index] = gradY/magn;
+            B_z[index] = gradZ/magn;
+        }}}
+    return 1;
+}
+
+float ProjectVect_func3D(float *R1, float *R2, float *R3, float *B_x, float *B_y, float *B_z, long dimX, long dimY, long dimZ)
+{
+    long i,j,k,index;
+    float in_prod;
+#pragma omp parallel for shared(R1, R2, R3, B_x, B_y, B_z) private(index,i,j,k,in_prod)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+			index = (dimX*dimY)*k + j*dimX+i;
+            in_prod = R1[index]*B_x[index] + R2[index]*B_y[index] + R3[index]*B_z[index];   /* calculate inner product */
+            R1[index] = R1[index] - in_prod*B_x[index];
+            R2[index] = R2[index] - in_prod*B_y[index];
+            R3[index] = R3[index] - in_prod*B_z[index];
+        }}}
+    return 1;
+}
+
+float Obj_dfunc3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, long dimX, long dimY, long dimZ)
+{
+    float val1, val2, val3;
+    long i,j,k,index;
+#pragma omp parallel for shared(A,D,R1,R2,R3) private(index,i,j,k,val1,val2,val3)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;
+                /* boundary conditions */
+                if (i == 0) {val1 = 0.0f;} else {val1 = R1[(dimX*dimY)*k + j*dimX + (i-1)];}
+                if (j == 0) {val2 = 0.0f;} else {val2 = R2[(dimX*dimY)*k + (j-1)*dimX + i];}
+                if (k == 0) {val3 = 0.0f;} else {val3 = R3[(dimX*dimY)*(k-1) + j*dimX + i];}
+                D[index] = A[index] - lambda*(R1[index] + R2[index] + R3[index] - val1 - val2 - val3);
+            }}}
+    return *D;
+}
+float Grad_dfunc3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float *B_x, float *B_y, float *B_z, float lambda, long dimX, long dimY, long dimZ)
+{
+    float val1, val2, val3, multip, in_prod;
+    long i,j,k, index;
+    multip = (1.0f/(26.0f*lambda));
+#pragma omp parallel for shared(P1,P2,P3,D,R1,R2,R3,multip) private(index,i,j,k,val1,val2,val3,in_prod)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;				
+                /* boundary conditions */
+                if (i == dimX-1) val1 = 0.0f; else val1 = D[index] - D[(dimX*dimY)*k + j*dimX + (i+1)];
+                if (j == dimY-1) val2 = 0.0f; else val2 = D[index] - D[(dimX*dimY)*k + (j+1)*dimX + i];
+                if (k == dimZ-1) val3 = 0.0f; else val3 = D[index] - D[(dimX*dimY)*(k+1) + j*dimX + i];
+                
+                in_prod = val1*B_x[index] + val2*B_y[index] + val3*B_z[index];   /* calculate inner product */
+                val1 = val1 - in_prod*B_x[index];
+                val2 = val2 - in_prod*B_y[index];
+                val3 = val3 - in_prod*B_z[index];
+                
+                P1[index] = R1[index] + multip*val1;
+                P2[index] = R2[index] + multip*val2;
+                P3[index] = R3[index] + multip*val3;
+            }}}
+    return 1;
+}
+float Proj_dfunc3D(float *P1, float *P2, float *P3, int methTV, long DimTotal)
+{		
+    float val1, val2, val3, denom, sq_denom;
+    long i;
+    if (methTV == 0) {
+	/* isotropic TV*/
+	#pragma omp parallel for shared(P1,P2,P3) private(i,val1,val2,val3,sq_denom)
+    for(i=0; i<DimTotal; i++) {        
+				denom = powf(P1[i],2) + powf(P2[i],2) + powf(P3[i],2);
+                if (denom > 1.0f) {
+					sq_denom = 1.0f/sqrtf(denom);
+                    P1[i] = P1[i]*sq_denom;
+                    P2[i] = P2[i]*sq_denom;
+                    P3[i] = P3[i]*sq_denom;
+                }
+			}
+	}    
+    else {
+    /* anisotropic TV*/
+#pragma omp parallel for shared(P1,P2,P3) private(i,val1,val2,val3)
+    for(i=0; i<DimTotal; i++) {
+                val1 = fabs(P1[i]);
+                val2 = fabs(P2[i]);
+                val3 = fabs(P3[i]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                if (val3 < 1.0f) {val3 = 1.0f;}                
+                P1[i] = P1[i]/val1;
+                P2[i] = P2[i]/val2;
+                P3[i] = P3[i]/val3;
+            }
+		}
+    return 1;
+}
+float Rupd_dfunc3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, long DimTotal)
+{
+    long i;
+    float multip;
+    multip = ((tk-1.0f)/tkp1);
+#pragma omp parallel for shared(P1,P2,P3,P1_old,P2_old,P3_old,R1,R2,R3,multip) private(i)
+    for(i=0; i<DimTotal; i++) {
+                R1[i] = P1[i] + multip*(P1[i] - P1_old[i]);
+                R2[i] = P2[i] + multip*(P2[i] - P2_old[i]);
+                R3[i] = P3[i] + multip*(P3[i] - P3_old[i]);
+            }
+    return 1;
+}
diff --git a/src/Core/regularisers_CPU/FGP_dTV_core.h b/src/Core/regularisers_CPU/FGP_dTV_core.h
new file mode 100644
index 0000000..442dd30
--- /dev/null
+++ b/src/Core/regularisers_CPU/FGP_dTV_core.h
@@ -0,0 +1,72 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+//#include <matrix.h>
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+/* C-OMP implementation of FGP-dTV [1,2] denoising/regularization model (2D/3D case)
+ * which employs structural similarity of the level sets of two images/volumes, see [1,2]
+ * The current implementation updates image 1 while image 2 is being fixed.
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. Additional reference image/volume of the same dimensions as (1) [REQUIRED]
+ * 3. lambdaPar - regularization parameter [REQUIRED]
+ * 4. Number of iterations [OPTIONAL]
+ * 5. eplsilon: tolerance constant [OPTIONAL]
+ * 6. eta: smoothing constant to calculate gradient of the reference [OPTIONAL] * 
+ * 7. TV-type: methodTV - 'iso' (0) or 'l1' (1) [OPTIONAL]
+ * 8. nonneg: 'nonnegativity (0 is OFF by default) [OPTIONAL]
+ * 9. print information: 0 (off) or 1 (on) [OPTIONAL]
+ *
+ * Output:
+ * [1] Filtered/regularized image/volume
+ *
+ * This function is based on the Matlab's codes and papers by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ * [2] M. J. Ehrhardt and M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), pp. 1084–1106
+ */
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float GradNorm_func2D(float *B, float *B_x, float *B_y, float eta, long dimX, long dimY);
+CCPI_EXPORT float ProjectVect_func2D(float *R1, float *R2, float *B_x, float *B_y, long dimX, long dimY);
+CCPI_EXPORT float Obj_dfunc2D(float *A, float *D, float *R1, float *R2, float lambda, long dimX, long dimY);
+CCPI_EXPORT float Grad_dfunc2D(float *P1, float *P2, float *D, float *R1, float *R2, float *B_x, float *B_y, float lambda, long dimX, long dimY);
+CCPI_EXPORT float Proj_dfunc2D(float *P1, float *P2, int methTV, long DimTotal);
+CCPI_EXPORT float Rupd_dfunc2D(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, long DimTotal);
+
+CCPI_EXPORT float GradNorm_func3D(float *B, float *B_x, float *B_y, float *B_z, float eta, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float ProjectVect_func3D(float *R1, float *R2, float *R3, float *B_x, float *B_y, float *B_z, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float Obj_dfunc3D(float *A, float *D, float *R1, float *R2, float *R3, float lambda, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float Grad_dfunc3D(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float *B_x, float *B_y, float *B_z, float lambda, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float Proj_dfunc3D(float *P1, float *P2, float *P3, int methTV, long DimTotal);
+CCPI_EXPORT float Rupd_dfunc3D(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, long DimTotal);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/LLT_ROF_core.c b/src/Core/regularisers_CPU/LLT_ROF_core.c
new file mode 100644
index 0000000..8416a14
--- /dev/null
+++ b/src/Core/regularisers_CPU/LLT_ROF_core.c
@@ -0,0 +1,410 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "LLT_ROF_core.h"
+#define EPS_LLT 0.01
+#define EPS_ROF 1.0e-12
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+/*sign function*/
+int signLLT(float x) {
+    return (x > 0) - (x < 0);
+}
+
+/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+ * 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
+{
+		long DimTotal;
+        int ll;
+		float *D1_LLT=NULL, *D2_LLT=NULL, *D3_LLT=NULL, *D1_ROF=NULL, *D2_ROF=NULL, *D3_ROF=NULL;
+		
+		DimTotal = (long)(dimX*dimY*dimZ);
+        
+        D1_ROF = calloc(DimTotal, sizeof(float));
+        D2_ROF = calloc(DimTotal, sizeof(float));
+        D3_ROF = calloc(DimTotal, sizeof(float));
+        
+        D1_LLT = calloc(DimTotal, sizeof(float));
+        D2_LLT = calloc(DimTotal, sizeof(float));
+        D3_LLT = calloc(DimTotal, sizeof(float));
+        
+        copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ)); /* initialize  */
+       
+		for(ll = 0; ll < iterationsNumb; ll++) {            
+            if (dimZ == 1) {
+			/* 2D case */
+			/****************ROF******************/
+			 /* calculate first-order differences */
+            D1_func_ROF(Output, D1_ROF, (long)(dimX), (long)(dimY), 1l);
+            D2_func_ROF(Output, D2_ROF, (long)(dimX), (long)(dimY), 1l);
+            /****************LLT******************/
+            /* estimate second-order derrivatives */
+            der2D_LLT(Output, D1_LLT, D2_LLT, (long)(dimX), (long)(dimY), 1l);
+            /* Joint update for ROF and LLT models */
+            Update2D_LLT_ROF(Input, Output, D1_LLT, D2_LLT, D1_ROF, D2_ROF, lambdaROF, lambdaLLT, tau, (long)(dimX), (long)(dimY), 1l);
+            }
+            else {
+			/* 3D case */
+			/* calculate first-order differences */
+            D1_func_ROF(Output, D1_ROF, (long)(dimX), (long)(dimY), (long)(dimZ));
+            D2_func_ROF(Output, D2_ROF, (long)(dimX), (long)(dimY), (long)(dimZ));
+            D3_func_ROF(Output, D3_ROF, (long)(dimX), (long)(dimY), (long)(dimZ)); 
+            /****************LLT******************/
+            /* estimate second-order derrivatives */
+            der3D_LLT(Output, D1_LLT, D2_LLT, D3_LLT,(long)(dimX), (long)(dimY), (long)(dimZ));
+            /* Joint update for ROF and LLT models */
+            Update3D_LLT_ROF(Input, Output, D1_LLT, D2_LLT, D3_LLT, D1_ROF, D2_ROF, D3_ROF, lambdaROF, lambdaLLT, tau, (long)(dimX), (long)(dimY), (long)(dimZ));
+			}
+        } /*end of iterations*/
+    free(D1_LLT);free(D2_LLT);free(D3_LLT);
+    free(D1_ROF);free(D2_ROF);free(D3_ROF);
+	return *Output;
+}
+
+/*************************************************************************/
+/**********************LLT-related functions *****************************/
+/*************************************************************************/
+float der2D_LLT(float *U, float *D1, float *D2, long dimX, long dimY, long dimZ)
+{
+	long i, j, index, i_p, i_m, j_m, j_p;
+	float dxx, dyy, denom_xx, denom_yy;
+#pragma omp parallel for shared(U,D1,D2) private(i, j, index, i_p, i_m, j_m, j_p, denom_xx, denom_yy, dxx, dyy)
+	for (i = 0; i<dimX; i++) {
+		for (j = 0; j<dimY; j++) {
+			index = j*dimX+i;
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+
+			dxx = U[j*dimX+i_p] - 2.0f*U[index] + U[j*dimX+i_m];
+			dyy = U[j_p*dimX+i] - 2.0f*U[index] + U[j_m*dimX+i];
+
+			denom_xx = fabs(dxx) + EPS_LLT;
+			denom_yy = fabs(dyy) + EPS_LLT;
+
+			D1[index] = dxx / denom_xx;
+			D2[index] = dyy / denom_yy;
+		}
+	}
+	return 1;
+}
+
+float der3D_LLT(float *U, float *D1, float *D2, float *D3, long dimX, long dimY, long dimZ)
+ {
+ 	long i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, index;
+ 	float dxx, dyy, dzz, denom_xx, denom_yy, denom_zz;
+ #pragma omp parallel for shared(U,D1,D2,D3) private(i, j, index, k, i_p, i_m, j_m, j_p, k_p, k_m, denom_xx, denom_yy, denom_zz, dxx, dyy, dzz)
+ 	for (i = 0; i<dimX; i++) {
+ 		for (j = 0; j<dimY; j++) {
+ 			for (k = 0; k<dimZ; k++) {
+				/* symmetric boundary conditions (Neuman) */
+				i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+				i_m = i - 1; if (i_m < 0) i_m = i + 1;
+				j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+				j_m = j - 1; if (j_m < 0) j_m = j + 1;
+ 				k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+ 				k_m = k - 1; if (k_m < 0) k_m = k + 1;
+				
+				index = (dimX*dimY)*k + j*dimX+i;
+ 
+ 				dxx = U[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*U[index] + U[(dimX*dimY)*k + j*dimX+i_m];
+ 				dyy = U[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k + j_m*dimX+i];
+ 				dzz = U[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k_m + j*dimX+i];
+ 
+ 				denom_xx = fabs(dxx) + EPS_LLT;
+ 				denom_yy = fabs(dyy) + EPS_LLT;
+ 				denom_zz = fabs(dzz) + EPS_LLT;
+ 
+ 				D1[index] = dxx / denom_xx;
+ 				D2[index] = dyy / denom_yy;
+ 				D3[index] = dzz / denom_zz;
+ 			}
+ 		}
+ 	}
+ 	return 1;
+ }
+
+/*************************************************************************/
+/**********************ROF-related functions *****************************/
+/*************************************************************************/
+
+/* calculate differences 1 */
+float D1_func_ROF(float *A, float *D1, long dimX, long dimY, long dimZ)
+{
+    float NOMx_1, NOMy_1, NOMy_0, NOMz_1, NOMz_0, denom1, denom2,denom3, T1;
+    long i,j,k,i1,i2,k1,j1,j2,k2,index;
+    
+    if (dimZ > 1) {
+#pragma omp parallel for shared (A, D1, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2, NOMx_1,NOMy_1,NOMy_0,NOMz_1,NOMz_0,denom1,denom2,denom3,T1)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                for(k=0; k<dimZ; k++) {
+					index = (dimX*dimY)*k + j*dimX+i;
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = A[(dimX*dimY)*k + j1*dimX + i] - A[index]; /* x+ */
+                    NOMy_1 = A[(dimX*dimY)*k + j*dimX + i1] - A[index]; /* y+ */
+                    /*NOMx_0 = (A[(i)*dimY + j] - A[(i2)*dimY + j]); */  /* x- */
+                    NOMy_0 = A[index] - A[(dimX*dimY)*k + j*dimX + i2]; /* y- */
+                    
+                    NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                    NOMz_0 = A[index] - A[(dimX*dimY)*k2 + j*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMx_1*NOMx_1;
+                    denom2 = 0.5f*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5f*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T1 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                    D1[index] = NOMx_1/T1;
+                }}}
+    }
+    else {
+#pragma omp parallel for shared (A, D1, dimX, dimY) private(i, j, i1, j1, i2, j2,NOMx_1,NOMy_1,NOMy_0,denom1,denom2,T1,index)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+				index = j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[j1*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[j*dimX + i1] - A[index]; /* y+ */
+                /*NOMx_0 = (A[(i)*dimY + j] - A[(i2)*dimY + j]); */ /* x- */
+                NOMy_0 = A[index] - A[(j)*dimX + i2]; /* y- */
+                
+                denom1 = NOMx_1*NOMx_1;
+                denom2 = 0.5f*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom2 = denom2*denom2;
+                T1 = sqrtf(denom1 + denom2 + EPS_ROF);
+                D1[index] = NOMx_1/T1;
+            }}
+    }
+    return *D1;
+}
+/* calculate differences 2 */
+float D2_func_ROF(float *A, float *D2, long dimX, long dimY, long dimZ)
+{
+    float NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2;
+    long i,j,k,i1,i2,k1,j1,j2,k2,index;
+    
+    if (dimZ > 1) {
+#pragma omp parallel for shared (A, D2, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2,  NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                for(k=0; k<dimZ; k++) {
+					index = (dimX*dimY)*k + j*dimX+i;
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = A[(dimX*dimY)*k + (j1)*dimX + i] - A[index]; /* x+ */
+                    NOMy_1 = A[(dimX*dimY)*k + (j)*dimX + i1] - A[index]; /* y+ */
+                    NOMx_0 = A[index] - A[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                    NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                    NOMz_0 = A[index] - A[(dimX*dimY)*k2 + (j)*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMy_1*NOMy_1;
+                    denom2 = 0.5f*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5f*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T2 = sqrtf(denom1 + denom2 + denom3 + EPS_ROF);
+                    D2[index] = NOMy_1/T2;
+                }}}
+    }
+    else {
+#pragma omp parallel for shared (A, D2, dimX, dimY) private(i, j, i1, j1, i2, j2, NOMx_1,NOMy_1,NOMx_0,denom1,denom2,T2,index)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+				index = j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[j1*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[j*dimX + i1] - A[index]; /* y+ */
+                NOMx_0 = A[index] - A[j2*dimX + i]; /* x- */
+                /*NOMy_0 = A[(i)*dimY + j] - A[(i)*dimY + j2]; */  /* y- */
+                
+                denom1 = NOMy_1*NOMy_1;
+                denom2 = 0.5f*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = denom2*denom2;
+                T2 = sqrtf(denom1 + denom2 + EPS_ROF);
+                D2[index] = NOMy_1/T2;
+            }}
+    }
+    return *D2;
+}
+
+/* calculate differences 3 */
+float D3_func_ROF(float *A, float *D3, long dimX, long dimY, long dimZ)
+{
+    float NOMx_1, NOMy_1, NOMx_0, NOMy_0, NOMz_1, denom1, denom2, denom3, T3;
+    long index,i,j,k,i1,i2,k1,j1,j2,k2;
+    
+#pragma omp parallel for shared (A, D3, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2,  NOMx_1, NOMy_1, NOMy_0, NOMx_0, NOMz_1, denom1, denom2, denom3, T3)
+    for(j=0; j<dimY; j++) {
+        for(i=0; i<dimX; i++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                k2 = k - 1; if (k2 < 0) k2 = k+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[(dimX*dimY)*k + (j1)*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[(dimX*dimY)*k + (j)*dimX + i1] - A[index]; /* y+ */
+                NOMy_0 = A[index] - A[(dimX*dimY)*k + (j)*dimX + i2]; /* y- */
+                NOMx_0 = A[index] - A[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                /*NOMz_0 = A[(dimX*dimY)*k + (i)*dimY + j] - A[(dimX*dimY)*k2 + (i)*dimY + j]; */ /* z- */
+                
+                denom1 = NOMz_1*NOMz_1;
+                denom2 = 0.5f*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = denom2*denom2;
+                denom3 = 0.5f*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom3 = denom3*denom3;
+                T3 = sqrtf(denom1 + denom2 + denom3 + EPS_ROF);
+                D3[index] = NOMz_1/T3;
+            }}}
+    return *D3;
+}
+
+/*************************************************************************/
+/**********************ROF-LLT-related functions *************************/
+/*************************************************************************/
+
+float Update2D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D1_ROF, float *D2_ROF, float lambdaROF, float lambdaLLT, float tau, long dimX, long dimY, long dimZ)
+{
+	long i, j, index, i_p, i_m, j_m, j_p;
+	float div, laplc, dxx, dyy, dv1, dv2;
+#pragma omp parallel for shared(U,U0) private(i, j, index, i_p, i_m, j_m, j_p, laplc, div, dxx, dyy, dv1, dv2)
+	for (i = 0; i<dimX; i++) {
+		for (j = 0; j<dimY; j++) {
+			index = j*dimX+i;
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+			
+			/*LLT-related part*/
+			dxx = D1_LLT[j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[j*dimX+i_m];
+			dyy = D2_LLT[j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[j_m*dimX+i];
+			laplc = dxx + dyy; /*build Laplacian*/
+			
+			/*ROF-related part*/
+			dv1 = D1_ROF[index] - D1_ROF[j_m*dimX + i];
+            dv2 = D2_ROF[index] - D2_ROF[j*dimX + i_m];
+			div = dv1 + dv2; /*build Divirgent*/
+            
+			/*combine all into one cost function to minimise */
+            U[index] += tau*(2.0f*lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+		}
+	}
+	return *U;
+}
+
+float Update3D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D3_LLT, float *D1_ROF, float *D2_ROF, float *D3_ROF, float lambdaROF, float lambdaLLT, float tau, long dimX, long dimY, long dimZ)
+{
+	long i, j, k, i_p, i_m, j_m, j_p, k_p, k_m, index;
+	float div, laplc, dxx, dyy, dzz, dv1, dv2, dv3;
+#pragma omp parallel for shared(U,U0) private(i, j, k, index, i_p, i_m, j_m, j_p, k_p, k_m, laplc, div, dxx, dyy, dzz, dv1, dv2, dv3)
+ 	for (i = 0; i<dimX; i++) {
+ 		for (j = 0; j<dimY; j++) {
+ 			for (k = 0; k<dimZ; k++) {
+				/* symmetric boundary conditions (Neuman) */
+				i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+				i_m = i - 1; if (i_m < 0) i_m = i + 1;
+				j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+				j_m = j - 1; if (j_m < 0) j_m = j + 1;
+ 				k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+ 				k_m = k - 1; if (k_m < 0) k_m = k + 1;
+			
+				index = (dimX*dimY)*k + j*dimX+i;
+			
+				/*LLT-related part*/
+				dxx = D1_LLT[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[(dimX*dimY)*k + j*dimX+i_m];
+				dyy = D2_LLT[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[(dimX*dimY)*k + j_m*dimX+i];
+				dzz = D3_LLT[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*D3_LLT[index] + D3_LLT[(dimX*dimY)*k_m + j*dimX+i];
+				laplc = dxx + dyy + dzz; /*build Laplacian*/
+			
+				/*ROF-related part*/
+				dv1 = D1_ROF[index] - D1_ROF[(dimX*dimY)*k + j_m*dimX+i];
+				dv2 = D2_ROF[index] - D2_ROF[(dimX*dimY)*k + j*dimX+i_m];
+				dv3 = D3_ROF[index] - D3_ROF[(dimX*dimY)*k_m + j*dimX+i];
+				div = dv1 + dv2 + dv3; /*build Divirgent*/
+            
+				/*combine all into one cost function to minimise */
+				U[index] += tau*(2.0f*lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+			}
+		}
+	}
+	return *U;
+}
+
diff --git a/src/Core/regularisers_CPU/LLT_ROF_core.h b/src/Core/regularisers_CPU/LLT_ROF_core.h
new file mode 100644
index 0000000..8e6591e
--- /dev/null
+++ b/src/Core/regularisers_CPU/LLT_ROF_core.h
@@ -0,0 +1,65 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+ * 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float der2D_LLT(float *U, float *D1, float *D2, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float der3D_LLT(float *U, float *D1, float *D2, float *D3, long dimX, long dimY, long dimZ);
+
+CCPI_EXPORT float D1_func_ROF(float *A, float *D1, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float D2_func_ROF(float *A, float *D2, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float D3_func_ROF(float *A, float *D3, long dimX, long dimY, long dimZ);
+
+CCPI_EXPORT float Update2D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D1_ROF, float *D2_ROF, float lambdaROF, float lambdaLLT, float tau, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float Update3D_LLT_ROF(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D3_LLT, float *D1_ROF, float *D2_ROF, float *D3_ROF, float lambdaROF, float lambdaLLT, float tau, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/Nonlocal_TV_core.c b/src/Core/regularisers_CPU/Nonlocal_TV_core.c
new file mode 100644
index 0000000..c4c9118
--- /dev/null
+++ b/src/Core/regularisers_CPU/Nonlocal_TV_core.c
@@ -0,0 +1,173 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC and Diamond Light Source Ltd. 
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ * Copyright 2018 Diamond Light Source Ltd. 
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "Nonlocal_TV_core.h"
+
+/* C-OMP implementation of non-local regulariser
+ * Weights and associated indices must be given as an input.
+ * Gauss-Seidel fixed point iteration requires ~ 3 iterations, so the main effort
+ * goes in pre-calculation of weights and selection of patches
+ *
+ *
+ * Input Parameters:
+ * 1. 2D/3D grayscale image/volume
+ * 2. AR_i - indeces of i neighbours
+ * 3. AR_j - indeces of j neighbours
+ * 4. AR_k - indeces of k neighbours (0 - for 2D case)
+ * 5. Weights_ij(k) - associated weights 
+ * 6. regularisation parameter
+ * 7. iterations number 
+ 
+ * Output:
+ * 1. denoised image/volume 	
+ * Elmoataz, Abderrahim, Olivier Lezoray, and Sébastien Bougleux. "Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing." IEEE Trans. Image Processing 17, no. 7 (2008): 1047-1060.
+ 
+ */
+/*****************************************************************************/
+
+float Nonlocal_TV_CPU_main(float *A_orig, float *Output, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int NumNeighb, float lambdaReg, int IterNumb)
+{
+
+    long i, j, k;
+    int iter;
+    lambdaReg = 1.0f/lambdaReg;
+         
+    /*****2D INPUT *****/
+    if (dimZ == 0) {
+	  copyIm(A_orig, Output, (long)(dimX), (long)(dimY), 1l);
+    /* for each pixel store indeces of the most similar neighbours (patches) */
+     for(iter=0; iter<IterNumb; iter++) {    
+#pragma omp parallel for shared (A_orig, Output, Weights, H_i, H_j, iter) private(i,j)
+      for(i=0; i<(long)(dimX); i++) {
+            for(j=0; j<(long)(dimY); j++) {              
+             /*NLM_H1_2D(Output, A_orig, H_i, H_j, Weights, i, j, (long)(dimX), (long)(dimY), NumNeighb, lambdaReg);*/  /* NLM - H1 penalty */
+             NLM_TV_2D(Output, A_orig, H_i, H_j, Weights, i, j, (long)(dimX), (long)(dimY), NumNeighb, lambdaReg);  /* NLM - TV penalty */
+           }}
+          }
+    }  
+    else {
+     /*****3D INPUT *****/
+        copyIm(A_orig, Output, (long)(dimX), (long)(dimY), (long)(dimZ));
+    /* for each pixel store indeces of the most similar neighbours (patches) */
+     for(iter=0; iter<IterNumb; iter++) {    
+#pragma omp parallel for shared (A_orig, Output, Weights, H_i, H_j, H_k, iter) private(i,j,k)
+      for(i=0; i<(long)(dimX); i++) {
+            for(j=0; j<(long)(dimY); j++) {              
+               for(k=0; k<(long)(dimZ); k++) {
+            /* NLM_H1_3D(Output, A_orig, H_i, H_j, H_k, Weights, i, j, k, dimX, dimY, dimZ, NumNeighb, lambdaReg); */ /* NLM - H1 penalty */
+            NLM_TV_3D(Output, A_orig, H_i, H_j, H_k, Weights, i, j, k, (long)(dimX), (long)(dimY), (long)(dimZ), NumNeighb, lambdaReg);   /* NLM - TV penalty */     
+           }}}          
+          }          
+    }
+    return *Output;
+}
+
+/***********<<<<Main Function for NLM - H1 penalty>>>>**********/
+float NLM_H1_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambdaReg)
+{
+	long x, i1, j1, index, index_m; 
+	float value = 0.0f, normweight  = 0.0f;
+	
+	index_m = j*dimX+i;
+	for(x=0; x < NumNeighb; x++) {
+	index =  (dimX*dimY*x) + j*dimX+i;
+		i1 = H_i[index];
+		j1 = H_j[index];
+		value += A[j1*dimX+i1]*Weights[index];
+		normweight += Weights[index];
+	}
+	 A[index_m] = (lambdaReg*A_orig[index_m] + value)/(lambdaReg + normweight);
+    return *A;
+}
+/*3D version*/
+float NLM_H1_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambdaReg)
+{
+	long x, i1, j1, k1, index; 
+	float value = 0.0f, normweight  = 0.0f;
+	
+	for(x=0; x < NumNeighb; x++) {
+	index = dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
+		i1 = H_i[index];
+		j1 = H_j[index];
+		k1 = H_k[index];
+		value += A[(dimX*dimY*k1) + j1*dimX+i1]*Weights[index];
+		normweight += Weights[index];
+	}	
+    A[(dimX*dimY*k) + j*dimX+i] = (lambdaReg*A_orig[(dimX*dimY*k) + j*dimX+i] + value)/(lambdaReg + normweight);
+    return *A;
+}
+
+
+/***********<<<<Main Function for NLM - TV penalty>>>>**********/
+float NLM_TV_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambdaReg)
+{
+	long x, i1, j1, index, index_m; 
+	float value = 0.0f, normweight  = 0.0f, NLgrad_magn = 0.0f, NLCoeff;
+	
+	 index_m = j*dimX+i;
+		
+	for(x=0; x < NumNeighb; x++) {
+		index =  (dimX*dimY*x) + j*dimX+i; /*c*/
+		i1 = H_i[index];
+		j1 = H_j[index];
+		NLgrad_magn += powf((A[j1*dimX+i1] - A[index_m]),2)*Weights[index];
+	}
+  
+    NLgrad_magn = sqrtf(NLgrad_magn); /*Non Local Gradients Magnitude */
+    NLCoeff = 2.0f*(1.0f/(NLgrad_magn + EPS));
+    		
+    for(x=0; x < NumNeighb; x++) {
+	index =  (dimX*dimY*x) + j*dimX+i; /*c*/
+	i1 = H_i[index];
+	j1 = H_j[index];
+        value += A[j1*dimX+i1]*NLCoeff*Weights[index];
+        normweight += Weights[index]*NLCoeff;
+    }   		
+    A[index_m] = (lambdaReg*A_orig[index_m] + value)/(lambdaReg + normweight);
+    return *A;
+}
+/*3D version*/
+float NLM_TV_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambdaReg)
+{
+	long x, i1, j1, k1, index; 
+	float value = 0.0f, normweight  = 0.0f, NLgrad_magn = 0.0f, NLCoeff;
+	
+	for(x=0; x < NumNeighb; x++) {
+	index =  dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
+		i1 = H_i[index];
+		j1 = H_j[index];
+		k1 = H_k[index];
+	        NLgrad_magn += powf((A[(dimX*dimY*k1) + j1*dimX+i1] - A[(dimX*dimY*k1) + j*dimX+i]),2)*Weights[index];
+	}
+  
+    NLgrad_magn = sqrtf(NLgrad_magn); /*Non Local Gradients Magnitude */
+    NLCoeff = 2.0f*(1.0f/(NLgrad_magn + EPS));
+    		
+    for(x=0; x < NumNeighb; x++) {
+	index = dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
+	i1 = H_i[index];
+	j1 = H_j[index];
+	k1 = H_k[index];
+        value += A[(dimX*dimY*k1) + j1*dimX+i1]*NLCoeff*Weights[index];
+        normweight += Weights[index]*NLCoeff;
+    }   		
+    A[(dimX*dimY*k) + j*dimX+i] = (lambdaReg*A_orig[(dimX*dimY*k) + j*dimX+i] + value)/(lambdaReg + normweight);
+    return *A;
+}
diff --git a/src/Core/regularisers_CPU/Nonlocal_TV_core.h b/src/Core/regularisers_CPU/Nonlocal_TV_core.h
new file mode 100644
index 0000000..6d55101
--- /dev/null
+++ b/src/Core/regularisers_CPU/Nonlocal_TV_core.h
@@ -0,0 +1,61 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC and Diamond Light Source Ltd. 
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ * Copyright 2018 Diamond Light Source Ltd. 
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+#define EPS 1.0000e-9
+
+/* C-OMP implementation of non-local regulariser
+ * Weights and associated indices must be given as an input.
+ * Gauss-Seidel fixed point iteration requires ~ 3 iterations, so the main effort
+ * goes in pre-calculation of weights and selection of patches
+ *
+ *
+ * Input Parameters:
+ * 1. 2D/3D grayscale image/volume
+ * 2. AR_i - indeces of i neighbours
+ * 3. AR_j - indeces of j neighbours
+ * 4. AR_k - indeces of k neighbours (0 - for 2D case)
+ * 5. Weights_ij(k) - associated weights 
+ * 6. regularisation parameter
+ * 7. iterations number 
+ 
+ * Output:
+ * 1. denoised image/volume 	
+ * Elmoataz, Abderrahim, Olivier Lezoray, and Sébastien Bougleux. "Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing." IEEE Trans.   Image Processing 17, no. 7 (2008): 1047-1060. 
+ */
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float Nonlocal_TV_CPU_main(float *A_orig, float *Output, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int NumNeighb, float lambdaReg, int IterNumb);
+CCPI_EXPORT float NLM_H1_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambdaReg);
+CCPI_EXPORT float NLM_TV_2D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, int NumNeighb, float lambdaReg);
+CCPI_EXPORT float NLM_H1_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambdaReg);
+CCPI_EXPORT float NLM_TV_3D(float *A, float *A_orig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimX, long dimY, long dimZ, int NumNeighb, float lambdaReg);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/PatchSelect_core.c b/src/Core/regularisers_CPU/PatchSelect_core.c
new file mode 100644
index 0000000..cf5cdc7
--- /dev/null
+++ b/src/Core/regularisers_CPU/PatchSelect_core.c
@@ -0,0 +1,345 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC and Diamond Light Source Ltd. 
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ * Copyright 2018 Diamond Light Source Ltd. 
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "PatchSelect_core.h"
+
+/* C-OMP implementation of non-local weight pre-calculation for non-local priors
+ * Weights and associated indices are stored into pre-allocated arrays and passed
+ * to the regulariser
+ *
+ *
+ * Input Parameters:
+ * 1. 2D/3D grayscale image/volume
+ * 2. Searching window (half-size of the main bigger searching window, e.g. 11)
+ * 3. Similarity window (half-size of the patch window, e.g. 2)
+ * 4. The number of neighbours to take (the most prominent after sorting neighbours will be taken)
+ * 5. noise-related parameter to calculate non-local weights
+ *
+ * Output [2D]:
+ * 1. AR_i - indeces of i neighbours
+ * 2. AR_j - indeces of j neighbours
+ * 3. Weights_ij - associated weights
+ *
+ * Output [3D]:
+ * 1. AR_i - indeces of i neighbours
+ * 2. AR_j - indeces of j neighbours
+ * 3. AR_k - indeces of j neighbours
+ * 4. Weights_ijk - associated weights
+ */
+
+void swap(float *xp, float *yp) 
+{ 
+    float temp = *xp; 
+    *xp = *yp; 
+    *yp = temp; 
+} 
+
+void swapUS(unsigned short *xp, unsigned short *yp) 
+{ 
+    unsigned short temp = *xp; 
+    *xp = *yp; 
+    *yp = temp; 
+} 
+/**************************************************/
+
+float PatchSelect_CPU_main(float *A, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h, int switchM)
+{
+    int counterG;
+    long i, j, k;
+    float *Eucl_Vec, h2;
+    h2 = h*h;   
+    /****************2D INPUT ***************/
+    if (dimZ == 0) {
+        /* generate a 2D Gaussian kernel for NLM procedure */
+        Eucl_Vec = (float*) calloc ((2*SimilarWin+1)*(2*SimilarWin+1),sizeof(float));
+        counterG = 0;
+        for(i=-SimilarWin; i<=SimilarWin; i++) {
+            for(j=-SimilarWin; j<=SimilarWin; j++) {
+                Eucl_Vec[counterG] = (float)exp(-(pow(((float) i), 2) + pow(((float) j), 2))/(2*SimilarWin*SimilarWin));
+                counterG++;
+            }} /*main neighb loop */
+        /* for each pixel store indeces of the most similar neighbours (patches) */
+        if (switchM == 1) {
+#pragma omp parallel for shared (A, Weights, H_i, H_j) private(i,j)
+    for(i=0; i<(long)(dimX); i++) {
+          for(j=0; j<(long)(dimY); j++) {
+                Indeces2D_p(A, H_i, H_j, Weights, i, j, (long)(dimX), (long)(dimY), Eucl_Vec, NumNeighb, SearchWindow, SimilarWin, h2);
+            }}
+        }
+        else {
+#pragma omp parallel for shared (A, Weights, H_i, H_j) private(i,j)
+    for(i=0; i<(long)(dimX); i++) {
+          for(j=0; j<(long)(dimY); j++) {
+                Indeces2D(A, H_i, H_j, Weights, i, j, (long)(dimX), (long)(dimY), Eucl_Vec, NumNeighb, SearchWindow, SimilarWin, h2);
+            }}
+            }
+    }
+    else {
+    /****************3D INPUT ***************/       
+        /* generate a 3D Gaussian kernel for NLM procedure */
+        Eucl_Vec = (float*) calloc ((2*SimilarWin+1)*(2*SimilarWin+1)*(2*SimilarWin+1),sizeof(float));
+        counterG = 0;
+        for(i=-SimilarWin; i<=SimilarWin; i++) {
+            for(j=-SimilarWin; j<=SimilarWin; j++) {
+                for(k=-SimilarWin; k<=SimilarWin; k++) {
+                    Eucl_Vec[counterG] = (float)exp(-(pow(((float) i), 2) + pow(((float) j), 2) + pow(((float) k), 2))/(2*SimilarWin*SimilarWin*SimilarWin));
+                    counterG++;
+                }}} /*main neighb loop */     
+        
+        /* for each voxel store indeces of the most similar neighbours (patches) */
+        if (switchM == 1) {
+#pragma omp parallel for shared (A, Weights, H_i, H_j, H_k) private(i,j,k)
+        for(i=0; i<dimX; i++) {
+            for(j=0; j<dimY; j++) {
+                for(k=0; k<dimZ; k++) {
+                    Indeces3D(A, H_i, H_j, H_k, Weights, j, i, (k), (dimX), (dimY), (dimZ), Eucl_Vec, NumNeighb, SearchWindow, SimilarWin, h2);
+                }}}
+        }
+        else {
+#pragma omp parallel for shared (A, Weights, H_i, H_j, H_k) private(i,j,k)
+        for(i=0; i<dimX; i++) {
+            for(j=0; j<dimY; j++) {
+                for(k=0; k<dimZ; k++) {
+                    Indeces3D(A, H_i, H_j, H_k, Weights, (i), (j), (k), (dimX), (dimY), (dimZ), Eucl_Vec, NumNeighb, SearchWindow, SimilarWin, h2);
+                }}}
+            }
+    }
+    free(Eucl_Vec);
+    return 1;
+}
+
+float Indeces2D(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2)
+{
+    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, index, sizeWin_tot, counterG;
+    float *Weight_Vec, normsum;
+    unsigned short *ind_i, *ind_j;
+    
+    sizeWin_tot = (2*SearchWindow + 1)*(2*SearchWindow + 1);
+    
+    Weight_Vec = (float*) calloc(sizeWin_tot, sizeof(float));
+    ind_i = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
+    ind_j = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
+    
+    counter = 0;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            i1 = i+i_m;
+            j1 = j+j_m;
+            if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) {
+                normsum = 0.0f; counterG = 0;
+                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                        i2 = i1 + i_c;
+                        j2 = j1 + j_c;
+                        i3 = i + i_c;
+                        j3 = j + j_c;
+                        if (((i2 >= 0) && (i2 < dimX)) && ((j2 >= 0) && (j2 < dimY))) {
+                            if (((i3 >= 0) && (i3 < dimX)) && ((j3 >= 0) && (j3 < dimY))) {
+                                normsum += Eucl_Vec[counterG]*pow(Aorig[j3*dimX + (i3)] - Aorig[j2*dimX + (i2)], 2);
+                                counterG++;
+                            }}
+                        
+                    }}
+                /* writing temporarily into vectors */
+                if (normsum > EPS) {                    
+                    Weight_Vec[counter] = expf(-normsum/h2);
+                    ind_i[counter] = i1;
+                    ind_j[counter] = j1;                    
+                    counter++;
+                }
+            }
+        }}     
+    /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter-1; x++)  {
+       for (y = 0; y < counter-x-1; y++)  {
+           if (Weight_Vec[y] < Weight_Vec[y+1]) {
+            swap(&Weight_Vec[y], &Weight_Vec[y+1]); 		
+            swapUS(&ind_i[y], &ind_i[y+1]);
+            swapUS(&ind_j[y], &ind_j[y+1]);  
+            }
+    	}
+    }
+     /*sorting loop finished*/      
+    /*now select the NumNeighb more prominent weights and store into pre-allocated arrays */ 
+    for(x=0; x < NumNeighb; x++) {
+        index = (dimX*dimY*x) + j*dimX+i;        
+        H_i[index] = ind_i[x];
+        H_j[index] = ind_j[x];
+        Weights[index] = Weight_Vec[x];
+    }    
+    free(ind_i);
+    free(ind_j);
+    free(Weight_Vec);
+    return 1;
+}
+float Indeces2D_p(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2)
+{
+    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, index, sizeWin_tot, counterG;
+    float *Weight_Vec, normsum;
+    unsigned short *ind_i, *ind_j;
+    
+    sizeWin_tot = (2*SearchWindow + 1)*(2*SearchWindow + 1);
+    
+    Weight_Vec = (float*) calloc(sizeWin_tot, sizeof(float));
+    ind_i = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
+    ind_j = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
+    
+    counter = 0;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            i1 = i+i_m;
+            j1 = j+j_m;
+            if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY))) {
+                normsum = 0.0f; counterG = 0;
+                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                        i2 = i1 + i_c;
+                        j2 = j1 + j_c;
+                        i3 = i + i_c;
+                        j3 = j + j_c;
+                        if (((i2 >= 0) && (i2 < dimX)) && ((j2 >= 0) && (j2 < dimY))) {
+                            if (((i3 >= 0) && (i3 < dimX)) && ((j3 >= 0) && (j3 < dimY))) {
+                                //normsum += Eucl_Vec[counterG]*pow(Aorig[j3*dimX + (i3)] - Aorig[j2*dimX + (i2)], 2);
+                                normsum += Eucl_Vec[counterG]*pow(Aorig[i3*dimY + (j3)] - Aorig[i2*dimY + (j2)], 2);
+                                counterG++;
+                            }}
+                        
+                    }}
+                /* writing temporarily into vectors */
+                if (normsum > EPS) {
+                    Weight_Vec[counter] = expf(-normsum/h2);
+                    ind_i[counter] = i1;
+                    ind_j[counter] = j1;
+                    counter++;
+                }
+            }
+        }}
+       /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter-1; x++)  {
+       for (y = 0; y < counter-x-1; y++)  {
+           if (Weight_Vec[y] < Weight_Vec[y+1]) {
+            swap(&Weight_Vec[y], &Weight_Vec[y+1]); 		
+            swapUS(&ind_i[y], &ind_i[y+1]);
+            swapUS(&ind_j[y], &ind_j[y+1]);  
+            }
+    	}
+    }
+    /*sorting loop finished*/
+    
+    /*now select the NumNeighb more prominent weights and store into pre-allocated arrays */ 
+    for(x=0; x < NumNeighb; x++) {
+        index = (dimX*dimY*x) + i*dimY+j;       
+        H_i[index] = ind_i[x];
+        H_j[index] = ind_j[x];
+        Weights[index] = Weight_Vec[x];
+    }   
+    free(ind_i);
+    free(ind_j);
+    free(Weight_Vec);
+    return 1;
+}
+
+float Indeces3D(float *Aorig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimY, long dimX, long dimZ, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2)
+{
+    long i1, j1, k1, i_m, j_m, k_m, i_c, j_c, k_c, i2, j2, k2, i3, j3, k3, counter, x, y, index, sizeWin_tot, counterG;
+    float *Weight_Vec, normsum, temp;
+    unsigned short *ind_i, *ind_j, *ind_k, temp_i, temp_j, temp_k;
+    
+    sizeWin_tot = (2*SearchWindow + 1)*(2*SearchWindow + 1)*(2*SearchWindow + 1);
+    
+    Weight_Vec = (float*) calloc(sizeWin_tot, sizeof(float));
+    ind_i = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
+    ind_j = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
+    ind_k = (unsigned short*) calloc(sizeWin_tot, sizeof(unsigned short));
+    
+    counter = 0l;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            for(k_m=-SearchWindow; k_m<=SearchWindow; k_m++) {
+                k1 = k+k_m;
+                i1 = i+i_m;
+                j1 = j+j_m;
+                if (((i1 >= 0) && (i1 < dimX)) && ((j1 >= 0) && (j1 < dimY)) && ((k1 >= 0) && (k1 < dimZ))) {
+                    normsum = 0.0f; counterG = 0l;
+                    for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                        for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                            for(k_c=-SimilarWin; k_c<=SimilarWin; k_c++) {
+                                i2 = i1 + i_c;
+                                j2 = j1 + j_c;
+                                k2 = k1 + k_c;
+                                i3 = i + i_c;
+                                j3 = j + j_c;
+                                k3 = k + k_c;
+                                if (((i2 >= 0) && (i2 < dimX)) && ((j2 >= 0) && (j2 < dimY)) && ((k2 >= 0) && (k2 < dimZ))) {
+                                    if (((i3 >= 0) && (i3 < dimX)) && ((j3 >= 0) && (j3 < dimY)) && ((k3 >= 0) && (k3 < dimZ))) {
+                                        normsum += Eucl_Vec[counterG]*pow(Aorig[(dimX*dimY*k3) + j3*dimX + (i3)] - Aorig[(dimX*dimY*k2) + j2*dimX + (i2)], 2);
+                                        counterG++;
+                                    }}
+                            }}}
+                    /* writing temporarily into vectors */
+                    if (normsum > EPS) {
+                        Weight_Vec[counter] = expf(-normsum/h2);
+                        ind_i[counter] = i1;
+                        ind_j[counter] = j1;
+                        ind_k[counter] = k1;
+                        counter ++;
+                    }
+                }
+            }}}
+    /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter; x++)  {
+        for (y = 0; y < counter; y++)  {
+            if (Weight_Vec[y] < Weight_Vec[x]) {
+                temp = Weight_Vec[y+1];
+                temp_i = ind_i[y+1];
+                temp_j = ind_j[y+1];
+                temp_k = ind_k[y+1];
+                Weight_Vec[y+1] = Weight_Vec[y];
+                Weight_Vec[y] = temp;
+                ind_i[y+1] = ind_i[y];
+                ind_i[y] = temp_i;
+                ind_j[y+1] = ind_j[y];
+                ind_j[y] = temp_j;
+                ind_k[y+1] = ind_k[y];
+                ind_k[y] = temp_k;
+            }}}
+    /*sorting loop finished*/
+    
+    /*now select the NumNeighb more prominent weights and store into arrays */
+    for(x=0; x < NumNeighb; x++) {
+        index = dimX*dimY*dimZ*x + (dimX*dimY*k) + j*dimX+i;
+        
+        H_i[index] = ind_i[x];
+        H_j[index] = ind_j[x];
+        H_k[index] = ind_k[x];
+        
+        Weights[index] = Weight_Vec[x];
+    }
+    
+    free(ind_i);
+    free(ind_j);
+    free(ind_k);
+    free(Weight_Vec);
+    return 1;
+}
+
diff --git a/src/Core/regularisers_CPU/PatchSelect_core.h b/src/Core/regularisers_CPU/PatchSelect_core.h
new file mode 100644
index 0000000..ddaa428
--- /dev/null
+++ b/src/Core/regularisers_CPU/PatchSelect_core.h
@@ -0,0 +1,63 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC and Diamond Light Source Ltd. 
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ * Copyright 2018 Diamond Light Source Ltd. 
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+#define EPS 1.0000e-12
+
+/* C-OMP implementation of non-local weight pre-calculation for non-local priors
+ * Weights and associated indices are stored into pre-allocated arrays and passed
+ * to the regulariser
+ *
+ *
+ * Input Parameters:
+ * 1. 2D/3D grayscale image/volume
+ * 2. Searching window (half-size of the main bigger searching window, e.g. 11)
+ * 3. Similarity window (half-size of the patch window, e.g. 2)
+ * 4. The number of neighbours to take (the most prominent after sorting neighbours will be taken)
+ * 5. noise-related parameter to calculate non-local weights
+ *
+ * Output [2D]:
+ * 1. AR_i - indeces of i neighbours
+ * 2. AR_j - indeces of j neighbours
+ * 3. Weights_ij - associated weights
+ *
+ * Output [3D]:
+ * 1. AR_i - indeces of i neighbours
+ * 2. AR_j - indeces of j neighbours
+ * 3. AR_k - indeces of j neighbours
+ * 4. Weights_ijk - associated weights
+ */
+/*****************************************************************************/
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float PatchSelect_CPU_main(float *A, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h, int switchM);
+CCPI_EXPORT float Indeces2D(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2);
+CCPI_EXPORT float Indeces2D_p(float *Aorig, unsigned short *H_i, unsigned short *H_j, float *Weights, long i, long j, long dimX, long dimY, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2);
+CCPI_EXPORT float Indeces3D(float *Aorig, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, long i, long j, long k, long dimY, long dimX, long dimZ, float *Eucl_Vec, int NumNeighb, int SearchWindow, int SimilarWin, float h2);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/ROF_TV_core.c b/src/Core/regularisers_CPU/ROF_TV_core.c
new file mode 100644
index 0000000..1858442
--- /dev/null
+++ b/src/Core/regularisers_CPU/ROF_TV_core.c
@@ -0,0 +1,289 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "ROF_TV_core.h"
+
+#define EPS 1.0e-12
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+/*sign function*/
+int sign(float x) {
+    return (x > 0) - (x < 0);
+}
+
+
+/* C-OMP implementation of ROF-TV denoising/regularization model [1] (2D/3D case)
+ *
+ * 
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED]
+ * 3. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended  [REQUIRED]
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+ */
+
+/* Running iterations of TV-ROF function */
+float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ)
+{
+    float *D1, *D2, *D3;
+    int i; 
+    long DimTotal;
+    DimTotal = (long)(dimX*dimY*dimZ);    
+    
+    D1 = calloc(DimTotal, sizeof(float));
+    D2 = calloc(DimTotal, sizeof(float));
+    D3 = calloc(DimTotal, sizeof(float));
+	   
+    /* copy into output */
+    copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ));
+        
+    /* start TV iterations */
+    for(i=0; i < iterationsNumb; i++) {            
+            /* calculate differences */
+            D1_func(Output, D1, (long)(dimX), (long)(dimY), (long)(dimZ));
+            D2_func(Output, D2, (long)(dimX), (long)(dimY), (long)(dimZ));
+            if (dimZ > 1) D3_func(Output, D3, (long)(dimX), (long)(dimY), (long)(dimZ)); 
+            TV_kernel(D1, D2, D3, Output, Input, lambdaPar, tau, (long)(dimX), (long)(dimY), (long)(dimZ));
+		}           
+    free(D1);free(D2); free(D3);
+    return *Output;
+}
+
+/* calculate differences 1 */
+float D1_func(float *A, float *D1, long dimX, long dimY, long dimZ)
+{
+    float NOMx_1, NOMy_1, NOMy_0, NOMz_1, NOMz_0, denom1, denom2,denom3, T1;
+    long i,j,k,i1,i2,k1,j1,j2,k2,index;
+    
+    if (dimZ > 1) {
+#pragma omp parallel for shared (A, D1, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2, NOMx_1,NOMy_1,NOMy_0,NOMz_1,NOMz_0,denom1,denom2,denom3,T1)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                for(k=0; k<dimZ; k++) {
+					index = (dimX*dimY)*k + j*dimX+i;
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = A[(dimX*dimY)*k + j1*dimX + i] - A[index]; /* x+ */
+                    NOMy_1 = A[(dimX*dimY)*k + j*dimX + i1] - A[index]; /* y+ */
+                    /*NOMx_0 = (A[(i)*dimY + j] - A[(i2)*dimY + j]); */  /* x- */
+                    NOMy_0 = A[index] - A[(dimX*dimY)*k + j*dimX + i2]; /* y- */
+                    
+                    NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                    NOMz_0 = A[index] - A[(dimX*dimY)*k2 + j*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMx_1*NOMx_1;
+                    denom2 = 0.5f*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5f*(sign(NOMz_1) + sign(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T1 = sqrt(denom1 + denom2 + denom3 + EPS);
+                    D1[index] = NOMx_1/T1;
+                }}}
+    }
+    else {
+#pragma omp parallel for shared (A, D1, dimX, dimY) private(i, j, i1, j1, i2, j2,NOMx_1,NOMy_1,NOMy_0,denom1,denom2,T1,index)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+				index = j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[j1*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[j*dimX + i1] - A[index]; /* y+ */
+                /*NOMx_0 = (A[(i)*dimY + j] - A[(i2)*dimY + j]); */ /* x- */
+                NOMy_0 = A[index] - A[(j)*dimX + i2]; /* y- */
+                
+                denom1 = NOMx_1*NOMx_1;
+                denom2 = 0.5f*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom2 = denom2*denom2;
+                T1 = sqrtf(denom1 + denom2 + EPS);
+                D1[index] = NOMx_1/T1;
+            }}
+    }
+    return *D1;
+}
+/* calculate differences 2 */
+float D2_func(float *A, float *D2, long dimX, long dimY, long dimZ)
+{
+    float NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2;
+    long i,j,k,i1,i2,k1,j1,j2,k2,index;
+    
+    if (dimZ > 1) {
+#pragma omp parallel for shared (A, D2, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2,  NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                for(k=0; k<dimZ; k++) {
+                    index = (dimX*dimY)*k + j*dimX+i;
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = A[(dimX*dimY)*k + (j1)*dimX + i] - A[index]; /* x+ */
+                    NOMy_1 = A[(dimX*dimY)*k + (j)*dimX + i1] - A[index]; /* y+ */
+                    NOMx_0 = A[index] - A[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                    NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                    NOMz_0 = A[index] - A[(dimX*dimY)*k2 + (j)*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMy_1*NOMy_1;
+                    denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5f*(sign(NOMz_1) + sign(NOMz_0))*(MIN(fabs(NOMz_1),fabs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T2 = sqrtf(denom1 + denom2 + denom3 + EPS);
+                    D2[index] = NOMy_1/T2;
+                }}}
+    }
+    else {
+#pragma omp parallel for shared (A, D2, dimX, dimY) private(i, j, i1, j1, i2, j2, NOMx_1,NOMy_1,NOMx_0,denom1,denom2,T2,index)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+		index = j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[j1*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[j*dimX + i1] - A[index]; /* y+ */
+                NOMx_0 = A[index] - A[j2*dimX + i]; /* x- */
+                /*NOMy_0 = A[(i)*dimY + j] - A[(i)*dimY + j2]; */  /* y- */
+                
+                denom1 = NOMy_1*NOMy_1;
+                denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = denom2*denom2;
+                T2 = sqrtf(denom1 + denom2 + EPS);
+                D2[index] = NOMy_1/T2;
+            }}
+    }
+    return *D2;
+}
+
+/* calculate differences 3 */
+float D3_func(float *A, float *D3, long dimX, long dimY, long dimZ)
+{
+    float NOMx_1, NOMy_1, NOMx_0, NOMy_0, NOMz_1, denom1, denom2, denom3, T3;
+    long index,i,j,k,i1,i2,k1,j1,j2,k2;
+    
+#pragma omp parallel for shared (A, D3, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2,  NOMx_1, NOMy_1, NOMy_0, NOMx_0, NOMz_1, denom1, denom2, denom3, T3)
+    for(j=0; j<dimY; j++) {
+        for(i=0; i<dimX; i++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                k2 = k - 1; if (k2 < 0) k2 = k+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = A[(dimX*dimY)*k + (j1)*dimX + i] - A[index]; /* x+ */
+                NOMy_1 = A[(dimX*dimY)*k + (j)*dimX + i1] - A[index]; /* y+ */
+                NOMy_0 = A[index] - A[(dimX*dimY)*k + (j)*dimX + i2]; /* y- */
+                NOMx_0 = A[index] - A[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                NOMz_1 = A[(dimX*dimY)*k1 + j*dimX + i] - A[index]; /* z+ */
+                /*NOMz_0 = A[(dimX*dimY)*k + (i)*dimY + j] - A[(dimX*dimY)*k2 + (i)*dimY + j]; */ /* z- */
+                
+                denom1 = NOMz_1*NOMz_1;
+                denom2 = 0.5f*(sign(NOMx_1) + sign(NOMx_0))*(MIN(fabs(NOMx_1),fabs(NOMx_0)));
+                denom2 = denom2*denom2;
+                denom3 = 0.5f*(sign(NOMy_1) + sign(NOMy_0))*(MIN(fabs(NOMy_1),fabs(NOMy_0)));
+                denom3 = denom3*denom3;
+                T3 = sqrtf(denom1 + denom2 + denom3 + EPS);
+                D3[index] = NOMz_1/T3;
+            }}}
+    return *D3;
+}
+
+/* calculate divergence */
+float TV_kernel(float *D1, float *D2, float *D3, float *B, float *A, float lambda, float tau, long dimX, long dimY, long dimZ)
+{
+    float dv1, dv2, dv3;
+    long index,i,j,k,i1,i2,k1,j1,j2,k2;
+    
+    if (dimZ > 1) {
+#pragma omp parallel for shared (D1, D2, D3, B, dimX, dimY, dimZ) private(index, i, j, k, i1, j1, k1, i2, j2, k2, dv1,dv2,dv3)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                for(k=0; k<dimZ; k++) {
+                    index = (dimX*dimY)*k + j*dimX+i;
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    /*divergence components */
+                    dv1 = D1[index] - D1[(dimX*dimY)*k + j2*dimX+i];
+                    dv2 = D2[index] - D2[(dimX*dimY)*k + j*dimX+i2];
+                    dv3 = D3[index] - D3[(dimX*dimY)*k2 + j*dimX+i];
+                    
+                    B[index] += tau*(2.0f*lambda*(dv1 + dv2 + dv3) - (B[index] - A[index]));   
+                }}}
+    }
+    else {
+#pragma omp parallel for shared (D1, D2, B, dimX, dimY) private(index, i, j, i1, j1, i2, j2,dv1,dv2)
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+                index = j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                
+                /* divergence components  */
+                dv1 = D1[index] - D1[j2*dimX + i];
+                dv2 = D2[index] - D2[j*dimX + i2];
+
+                B[index] += tau*(2.0f*lambda*(dv1 + dv2) - (B[index] - A[index]));
+            }}
+    }
+    return *B;
+}
diff --git a/src/Core/regularisers_CPU/ROF_TV_core.h b/src/Core/regularisers_CPU/ROF_TV_core.h
new file mode 100644
index 0000000..4e320e9
--- /dev/null
+++ b/src/Core/regularisers_CPU/ROF_TV_core.h
@@ -0,0 +1,57 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+/* C-OMP implementation of ROF-TV denoising/regularization model [1] (2D/3D case)
+ *
+ * 
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED]
+ * 3. Number of iterations, for explicit scheme >= 150 is recommended  [REQUIRED]
+ * 4. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+ *
+ * D. Kazantsev, 2016-18
+ */
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float TV_kernel(float *D1, float *D2, float *D3, float *B, float *A, float lambda, float tau, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float D1_func(float *A, float *D1, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float D2_func(float *A, float *D2, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float D3_func(float *A, float *D3, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
\ No newline at end of file
diff --git a/src/Core/regularisers_CPU/SB_TV_core.c b/src/Core/regularisers_CPU/SB_TV_core.c
new file mode 100755
index 0000000..769ea67
--- /dev/null
+++ b/src/Core/regularisers_CPU/SB_TV_core.c
@@ -0,0 +1,368 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "SB_TV_core.h"
+
+/* C-OMP implementation of Split Bregman - TV denoising-regularisation model (2D/3D) [1]
+*
+* Input Parameters:
+* 1. Noisy image/volume
+* 2. lambda - regularisation parameter
+* 3. Number of iterations [OPTIONAL parameter]
+* 4. eplsilon - tolerance constant [OPTIONAL parameter]
+* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
+* 6. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+*
+* Output:
+* 1. Filtered/regularized image
+*
+* [1]. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.
+*/
+ 
+float SB_TV_CPU_main(float *Input, float *Output, float mu, int iter, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ)
+{
+	int ll;
+    long j, DimTotal;    
+	float re, re1, lambda;
+    int count = 0;
+    mu = 1.0f/mu;
+    lambda = 2.0f*mu;
+
+	if (dimZ <= 1) {
+		/* 2D case */
+		float *Output_prev=NULL, *Dx=NULL, *Dy=NULL, *Bx=NULL, *By=NULL;
+		DimTotal = (long)(dimX*dimY);
+		
+		Output_prev = calloc(DimTotal, sizeof(float));
+		Dx = calloc(DimTotal, sizeof(float));
+		Dy = calloc(DimTotal, sizeof(float));
+		Bx = calloc(DimTotal, sizeof(float));
+		By = calloc(DimTotal, sizeof(float));
+        
+        copyIm(Input, Output, (long)(dimX), (long)(dimY), 1l); /*initialize */
+        
+        /* begin outer SB iterations */
+        for(ll=0; ll<iter; ll++) {
+            
+            /* storing old estimate */
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), 1l);
+            
+            /* perform two GS iterations (normally 2 is enough for the convergence) */
+            gauss_seidel2D(Output, Input, Output_prev, Dx, Dy, Bx, By, (long)(dimX), (long)(dimY), lambda, mu);
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), 1l);
+            /*GS iteration */
+            gauss_seidel2D(Output, Input, Output_prev, Dx, Dy, Bx, By, (long)(dimX), (long)(dimY), lambda, mu);
+            
+            /* TV-related step */
+            if (methodTV == 1)  updDxDy_shrinkAniso2D(Output, Dx, Dy, Bx, By, (long)(dimX), (long)(dimY), lambda);
+            else updDxDy_shrinkIso2D(Output, Dx, Dy, Bx, By, (long)(dimX), (long)(dimY), lambda);
+            
+            /* update for Bregman variables */
+            updBxBy2D(Output, Dx, Dy, Bx, By, (long)(dimX), (long)(dimY));
+            
+            /* check early stopping criteria if epsilon not equal zero */
+            if (epsil != 0) {
+            re = 0.0f; re1 = 0.0f;
+				for(j=0; j<DimTotal; j++) {
+                re += pow(Output[j] - Output_prev[j],2);
+                re1 += pow(Output[j],2);
+				}
+            re = sqrt(re)/sqrt(re1);
+            if (re < epsil)  count++;
+				if (count > 4) break;
+			}
+            /*printf("%f %i %i \n", re, ll, count); */
+        }
+        if (printM == 1) printf("SB-TV iterations stopped at iteration %i \n", ll);
+		free(Output_prev); free(Dx); free(Dy); free(Bx); free(By);
+	}
+	else {
+		/* 3D case */
+		float *Output_prev=NULL, *Dx=NULL, *Dy=NULL, *Dz=NULL, *Bx=NULL, *By=NULL, *Bz=NULL;
+		DimTotal = (long)(dimX*dimY*dimZ);
+		
+		Output_prev = calloc(DimTotal, sizeof(float));
+		Dx = calloc(DimTotal, sizeof(float));
+		Dy = calloc(DimTotal, sizeof(float));
+		Dz = calloc(DimTotal, sizeof(float));
+		Bx = calloc(DimTotal, sizeof(float));
+		By = calloc(DimTotal, sizeof(float));
+		Bz = calloc(DimTotal, sizeof(float));
+        
+        copyIm(Input, Output, (long)(dimX), (long)(dimY), (long)(dimZ)); /*initialize */
+        
+        /* begin outer SB iterations */
+        for(ll=0; ll<iter; ll++) {
+            
+            /* storing old estimate */
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+             /* perform two GS iterations (normally 2 is enough for the convergence) */
+            gauss_seidel3D(Output, Input, Output_prev, Dx, Dy, Dz, Bx, By, Bz, (long)(dimX), (long)(dimY), (long)(dimZ), lambda, mu);
+            copyIm(Output, Output_prev, (long)(dimX), (long)(dimY), (long)(dimZ));
+            /*GS iteration */
+            gauss_seidel3D(Output, Input, Output_prev, Dx, Dy, Dz, Bx, By, Bz, (long)(dimX), (long)(dimY), (long)(dimZ), lambda, mu);
+            
+            /* TV-related step */
+            if (methodTV == 1)  updDxDyDz_shrinkAniso3D(Output, Dx, Dy, Dz, Bx, By, Bz, (long)(dimX), (long)(dimY), (long)(dimZ), lambda);
+            else updDxDyDz_shrinkIso3D(Output, Dx, Dy, Dz, Bx, By, Bz, (long)(dimX), (long)(dimY), (long)(dimZ), lambda);
+            
+            /* update for Bregman variables */
+            updBxByBz3D(Output, Dx, Dy, Dz, Bx, By, Bz, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /* check early stopping criteria if epsilon not equal zero */
+            if (epsil != 0) {
+            re = 0.0f; re1 = 0.0f;
+            for(j=0; j<DimTotal; j++) {
+                re += pow(Output[j] - Output_prev[j],2);
+                re1 += pow(Output[j],2);
+				}
+            re = sqrt(re)/sqrt(re1);
+            if (re < epsil)  count++;
+				if (count > 4) break;
+			}
+            /*printf("%f %i %i \n", re, ll, count); */
+        }
+        if (printM == 1) printf("SB-TV iterations stopped at iteration %i \n", ll);
+		free(Output_prev); free(Dx); free(Dy); free(Dz); free(Bx); free(By); free(Bz);
+	}
+	return *Output;
+}
+
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+float gauss_seidel2D(float *U, float *A, float *U_prev, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY, float lambda, float mu)
+{
+    float sum, normConst;
+    long i,j,i1,i2,j1,j2,index;
+    normConst = 1.0f/(mu + 4.0f*lambda);
+    
+#pragma omp parallel for shared(U) private(index,i,j,i1,i2,j1,j2,sum)
+    for(i=0; i<dimX; i++) {
+        /* symmetric boundary conditions (Neuman) */
+        i1 = i+1; if (i1 == dimX) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            index = j*dimX+i;
+            
+            sum = Dx[j*dimX+i2] - Dx[index] + Dy[j2*dimX+i] - Dy[index] - Bx[j*dimX+i2] + Bx[index] - By[j2*dimX+i] + By[index];
+            sum += U_prev[j*dimX+i1] + U_prev[j*dimX+i2] + U_prev[j1*dimX+i] + U_prev[j2*dimX+i];
+            sum *= lambda;
+            sum += mu*A[index];
+            U[index] = normConst*sum;
+        }}
+    return *U;
+}
+
+float updDxDy_shrinkAniso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY, float lambda)
+{
+    long i,j,i1,j1,index;
+    float val1, val11, val2, val22, denom_lam;
+    denom_lam = 1.0f/lambda;
+#pragma omp parallel for shared(U,denom_lam) private(index,i,j,i1,j1,val1,val11,val2,val22)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            i1 = i+1; if (i1 == dimX) i1 = i-1;
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            index = j*dimX+i;
+            
+            val1 = (U[j*dimX+i1] - U[index]) + Bx[index];
+            val2 = (U[j1*dimX+i] - U[index]) + By[index];
+            
+            val11 = fabs(val1) - denom_lam; if (val11 < 0) val11 = 0;
+            val22 = fabs(val2) - denom_lam; if (val22 < 0) val22 = 0;
+            
+            if (val1 !=0) Dx[index] = (val1/fabs(val1))*val11; else Dx[index] = 0;
+            if (val2 !=0) Dy[index] = (val2/fabs(val2))*val22; else Dy[index] = 0;
+            
+        }}
+    return 1;
+}
+float updDxDy_shrinkIso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY, float lambda)
+{
+    long i,j,i1,j1,index;
+    float val1, val11, val2, denom, denom_lam;
+    denom_lam = 1.0f/lambda;
+    
+#pragma omp parallel for shared(U,denom_lam) private(index,i,j,i1,j1,val1,val11,val2,denom)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            i1 = i+1; if (i1 == dimX) i1 = i-1;
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            index = j*dimX+i;
+            
+            val1 = (U[j*dimX+i1] - U[index]) + Bx[index];
+            val2 = (U[j1*dimX+i] - U[index]) + By[index];
+            
+            denom = sqrt(val1*val1 + val2*val2);
+            
+            val11 = (denom - denom_lam); if (val11 < 0) val11 = 0.0f;
+            
+            if (denom != 0.0f) {
+                Dx[index] = val11*(val1/denom);
+                Dy[index] = val11*(val2/denom);
+            }
+            else {
+                Dx[index] = 0;
+                Dy[index] = 0;
+            }
+        }}
+    return 1;
+}
+float updBxBy2D(float *U, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY)
+{
+    long i,j,i1,j1,index;
+#pragma omp parallel for shared(U) private(index,i,j,i1,j1)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            /* symmetric boundary conditions (Neuman) */
+            i1 = i+1; if (i1 == dimX) i1 = i-1;
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            index = j*dimX+i;
+            
+            Bx[index] += (U[j*dimX+i1] - U[index]) - Dx[index];
+            By[index] += (U[j1*dimX+i] - U[index]) - Dy[index];
+        }}
+    return 1;
+}
+
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+/*****************************************************************/
+float gauss_seidel3D(float *U, float *A, float *U_prev, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ, float lambda, float mu)
+{
+    float normConst, d_val, b_val, sum;
+    long i,j,i1,i2,j1,j2,k,k1,k2,index;
+    normConst = 1.0f/(mu + 6.0f*lambda);
+#pragma omp parallel for shared(U) private(index,i,j,i1,i2,j1,j2,k,k1,k2,d_val,b_val,sum)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i+1; if (i1 == dimX) i1 = i-1;
+                i2 = i-1; if (i2 < 0) i2 = i+1;
+                j1 = j+1; if (j1 == dimY) j1 = j-1;
+                j2 = j-1; if (j2 < 0) j2 = j+1;
+                k1 = k+1; if (k1 == dimZ) k1 = k-1;
+                k2 = k-1; if (k2 < 0) k2 = k+1;
+                index = (dimX*dimY)*k + j*dimX+i;
+                
+                d_val = Dx[(dimX*dimY)*k + j*dimX+i2] - Dx[index] + Dy[(dimX*dimY)*k + j2*dimX+i] - Dy[index] + Dz[(dimX*dimY)*k2 + j*dimX+i] - Dz[index];
+                b_val = -Bx[(dimX*dimY)*k + j*dimX+i2] + Bx[index] - By[(dimX*dimY)*k + j2*dimX+i] + By[index] - Bz[(dimX*dimY)*k2 + j*dimX+i] + Bz[index];
+                sum = d_val + b_val;
+                sum += U_prev[(dimX*dimY)*k + j*dimX+i1] + U_prev[(dimX*dimY)*k + j*dimX+i2] + U_prev[(dimX*dimY)*k + j1*dimX+i] + U_prev[(dimX*dimY)*k + j2*dimX+i] + U_prev[(dimX*dimY)*k1 + j*dimX+i] + U_prev[(dimX*dimY)*k2 + j*dimX+i];
+                sum *= lambda;
+                sum += mu*A[index];
+                U[index] = normConst*sum;
+            }}}
+    return *U;
+}
+
+float updDxDyDz_shrinkAniso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ, float lambda)
+{
+    long i,j,i1,j1,k,k1,index;
+    float val1, val11, val2, val22, val3, val33, denom_lam;
+    denom_lam = 1.0f/lambda;
+#pragma omp parallel for shared(U,denom_lam) private(index,i,j,i1,j1,k,k1,val1,val11,val2,val22,val3,val33)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+                index = (dimX*dimY)*k + j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i+1; if (i1 == dimX) i1 = i-1;
+                j1 = j+1; if (j1 == dimY) j1 = j-1;
+                k1 = k+1; if (k1 == dimZ) k1 = k-1;
+                
+                val1 = (U[(dimX*dimY)*k + j*dimX+i1] - U[index]) + Bx[index];
+                val2 = (U[(dimX*dimY)*k + j1*dimX+i] - U[index]) + By[index];
+                val3 = (U[(dimX*dimY)*k1 + j*dimX+i] - U[index]) + Bz[index];
+                
+                val11 = fabs(val1) - denom_lam; if (val11 < 0.0f) val11 = 0.0f;
+                val22 = fabs(val2) - denom_lam; if (val22 < 0.0f) val22 = 0.0f;
+                val33 = fabs(val3) - denom_lam; if (val33 < 0.0f) val33 = 0.0f;
+                
+                if (val1 !=0.0f) Dx[index] = (val1/fabs(val1))*val11; else Dx[index] = 0.0f;
+                if (val2 !=0.0f) Dy[index] = (val2/fabs(val2))*val22; else Dy[index] = 0.0f;
+                if (val3 !=0.0f) Dz[index] = (val3/fabs(val3))*val33; else Dz[index] = 0.0f;
+                
+            }}}
+    return 1;
+}
+float updDxDyDz_shrinkIso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ, float lambda)
+{
+    long i,j,i1,j1,k,k1,index;
+    float val1, val11, val2, val3, denom, denom_lam;
+    denom_lam = 1.0f/lambda;
+#pragma omp parallel for shared(U,denom_lam) private(index,denom,i,j,i1,j1,k,k1,val1,val11,val2,val3)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+                index = (dimX*dimY)*k + j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i+1; if (i1 == dimX) i1 = i-1;
+                j1 = j+1; if (j1 == dimY) j1 = j-1;
+                k1 = k+1; if (k1 == dimZ) k1 = k-1;
+                
+                val1 = (U[(dimX*dimY)*k + j*dimX+i1] - U[index]) + Bx[index];
+                val2 = (U[(dimX*dimY)*k + j1*dimX+i] - U[index]) + By[index];
+                val3 = (U[(dimX*dimY)*k1 + j*dimX+i] - U[index]) + Bz[index];
+                
+                denom = sqrt(val1*val1 + val2*val2 + val3*val3);
+                
+                val11 = (denom - denom_lam); if (val11 < 0) val11 = 0.0f;
+                
+                if (denom != 0.0f) {
+                    Dx[index] = val11*(val1/denom);
+                    Dy[index] = val11*(val2/denom);
+                    Dz[index] = val11*(val3/denom);
+                }
+                else {
+                    Dx[index] = 0;
+                    Dy[index] = 0;
+                    Dz[index] = 0;
+                }
+            }}}
+    return 1;
+}
+float updBxByBz3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ)
+{
+    long i,j,k,i1,j1,k1,index;
+#pragma omp parallel for shared(U) private(index,i,j,k,i1,j1,k1)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            for(k=0; k<dimZ; k++) {
+				index = (dimX*dimY)*k + j*dimX+i;
+                /* symmetric boundary conditions (Neuman) */
+                i1 = i+1; if (i1 == dimX) i1 = i-1;
+                j1 = j+1; if (j1 == dimY) j1 = j-1;
+                k1 = k+1; if (k1 == dimZ) k1 = k-1;
+                
+                Bx[index] += (U[(dimX*dimY)*k + j*dimX+i1] - U[index]) - Dx[index];
+                By[index] += (U[(dimX*dimY)*k + j1*dimX+i] - U[index]) - Dy[index];
+                Bz[index] += (U[(dimX*dimY)*k1 + j*dimX+i] - U[index]) - Dz[index];
+            }}}
+    return 1;
+}
diff --git a/src/Core/regularisers_CPU/SB_TV_core.h b/src/Core/regularisers_CPU/SB_TV_core.h
new file mode 100644
index 0000000..7485e3b
--- /dev/null
+++ b/src/Core/regularisers_CPU/SB_TV_core.h
@@ -0,0 +1,61 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+
+/* C-OMP implementation of Split Bregman - TV denoising-regularisation model (2D/3D) [1]
+*
+* Input Parameters:
+* 1. Noisy image/volume
+* 2. lambda - regularisation parameter
+* 3. Number of iterations [OPTIONAL parameter]
+* 4. eplsilon - tolerance constant [OPTIONAL parameter]
+* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
+* 6. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+*
+* Output:
+* 1. Filtered/regularized image
+*
+* [1]. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.
+*/
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float SB_TV_CPU_main(float *Input, float *Output, float mu, int iter, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
+
+CCPI_EXPORT float gauss_seidel2D(float *U, float *A, float *U_prev, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY, float lambda, float mu);
+CCPI_EXPORT float updDxDy_shrinkAniso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY, float lambda);
+CCPI_EXPORT float updDxDy_shrinkIso2D(float *U, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY, float lambda);
+CCPI_EXPORT float updBxBy2D(float *U, float *Dx, float *Dy, float *Bx, float *By, long dimX, long dimY);
+
+CCPI_EXPORT float gauss_seidel3D(float *U, float *A, float *U_prev, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ, float lambda, float mu);
+CCPI_EXPORT float updDxDyDz_shrinkAniso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ, float lambda);
+CCPI_EXPORT float updDxDyDz_shrinkIso3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ, float lambda);
+CCPI_EXPORT float updBxByBz3D(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/TGV_core.c b/src/Core/regularisers_CPU/TGV_core.c
new file mode 100644
index 0000000..805c3d4
--- /dev/null
+++ b/src/Core/regularisers_CPU/TGV_core.c
@@ -0,0 +1,487 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "TGV_core.h"
+
+/* C-OMP implementation of Primal-Dual denoising method for 
+ * Total Generilized Variation (TGV)-L2 model [1] (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume (2D/3D)
+ * 2. lambda - regularisation parameter
+ * 3. parameter to control the first-order term (alpha1)
+ * 4. parameter to control the second-order term (alpha0)
+ * 5. Number of Chambolle-Pock (Primal-Dual) iterations
+ * 6. Lipshitz constant (default is 12)
+ * 
+ * Output:
+ * Filtered/regularised image/volume
+ *
+ * References:
+ * [1] K. Bredies "Total Generalized Variation"
+ * 
+ */
+ 
+float TGV_main(float *U0, float *U, float lambda, float alpha1, float alpha0, int iter, float L2, int dimX, int dimY, int dimZ)
+{
+	long DimTotal;
+	int ll;
+	float *U_old, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, tau, sigma;
+
+	DimTotal = (long)(dimX*dimY*dimZ);
+	copyIm(U0, U, (long)(dimX), (long)(dimY), (long)(dimZ)); /* initialize */
+        tau = pow(L2,-0.5);
+        sigma = pow(L2,-0.5);
+
+        /* dual variables */
+        P1 = calloc(DimTotal, sizeof(float));
+        P2 = calloc(DimTotal, sizeof(float));
+        
+        Q1 = calloc(DimTotal, sizeof(float));
+        Q2 = calloc(DimTotal, sizeof(float));
+        Q3 = calloc(DimTotal, sizeof(float));
+        
+        U_old = calloc(DimTotal, sizeof(float));
+        
+        V1 = calloc(DimTotal, sizeof(float));
+        V1_old = calloc(DimTotal, sizeof(float));
+        V2 = calloc(DimTotal, sizeof(float));
+        V2_old = calloc(DimTotal, sizeof(float));
+	
+	if (dimZ == 1) {
+	/*2D case*/
+	
+        /* Primal-dual iterations begin here */
+        for(ll = 0; ll < iter; ll++) {
+            
+            /* Calculate Dual Variable P */
+            DualP_2D(U, V1, V2, P1, P2, (long)(dimX), (long)(dimY), sigma);
+            
+            /*Projection onto convex set for P*/
+            ProjP_2D(P1, P2, (long)(dimX), (long)(dimY), alpha1);
+            
+            /* Calculate Dual Variable Q */
+            DualQ_2D(V1, V2, Q1, Q2, Q3, (long)(dimX), (long)(dimY), sigma);
+            
+            /*Projection onto convex set for Q*/
+            ProjQ_2D(Q1, Q2, Q3, (long)(dimX), (long)(dimY), alpha0);
+            
+            /*saving U into U_old*/
+            copyIm(U, U_old, (long)(dimX), (long)(dimY), 1l);
+            
+            /*adjoint operation  -> divergence and projection of P*/
+            DivProjP_2D(U, U0, P1, P2, (long)(dimX), (long)(dimY), lambda, tau);
+            
+            /*get updated solution U*/
+            newU(U, U_old, (long)(dimX), (long)(dimY));
+            
+            /*saving V into V_old*/
+            copyIm(V1, V1_old, (long)(dimX), (long)(dimY), 1l);
+            copyIm(V2, V2_old, (long)(dimX), (long)(dimY), 1l);
+            
+            /* upd V*/
+            UpdV_2D(V1, V2, P1, P2, Q1, Q2, Q3, (long)(dimX), (long)(dimY), tau);
+            
+            /*get new V*/
+            newU(V1, V1_old, (long)(dimX), (long)(dimY));
+            newU(V2, V2_old, (long)(dimX), (long)(dimY));
+        } /*end of iterations*/
+        	}
+        else {
+        /*3D case*/
+        float *P3, *Q4, *Q5, *Q6, *V3, *V3_old;
+        
+        P3 = calloc(DimTotal, sizeof(float));
+        Q4 = calloc(DimTotal, sizeof(float));
+        Q5 = calloc(DimTotal, sizeof(float));
+        Q6 = calloc(DimTotal, sizeof(float));
+        V3 = calloc(DimTotal, sizeof(float));
+        V3_old = calloc(DimTotal, sizeof(float));
+        
+         /* Primal-dual iterations begin here */
+        for(ll = 0; ll < iter; ll++) {
+            
+            /* Calculate Dual Variable P */
+            DualP_3D(U, V1, V2, V3, P1, P2, P3, (long)(dimX), (long)(dimY), (long)(dimZ), sigma);
+            
+            /*Projection onto convex set for P*/
+            ProjP_3D(P1, P2, P3, (long)(dimX), (long)(dimY), (long)(dimZ), alpha1);
+            
+            /* Calculate Dual Variable Q */
+            DualQ_3D(V1, V2, V3, Q1, Q2, Q3, Q4, Q5, Q6, (long)(dimX), (long)(dimY), (long)(dimZ), sigma);
+            
+            /*Projection onto convex set for Q*/
+            ProjQ_3D(Q1, Q2, Q3, Q4, Q5, Q6, (long)(dimX), (long)(dimY), (long)(dimZ), alpha0);
+            
+            /*saving U into U_old*/
+            copyIm(U, U_old, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /*adjoint operation  -> divergence and projection of P*/
+            DivProjP_3D(U, U0, P1, P2, P3, (long)(dimX), (long)(dimY), (long)(dimZ), lambda, tau);
+            
+            /*get updated solution U*/
+            newU3D(U, U_old, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /*saving V into V_old*/
+            copyIm_3Ar(V1, V2, V3, V1_old, V2_old, V3_old, (long)(dimX), (long)(dimY), (long)(dimZ));
+            
+            /* upd V*/
+            UpdV_3D(V1, V2, V3, P1, P2, P3, Q1, Q2, Q3, Q4, Q5, Q6, (long)(dimX), (long)(dimY), (long)(dimZ), tau);
+            
+            /*get new V*/
+            newU3D_3Ar(V1, V2, V3, V1_old, V2_old, V3_old, (long)(dimX), (long)(dimY), (long)(dimZ));           
+	        } /*end of iterations*/
+        free(P3);free(Q4);free(Q5);free(Q6);free(V3);free(V3_old);
+        }     
+
+    /*freeing*/
+    free(P1);free(P2);free(Q1);free(Q2);free(Q3);free(U_old);
+    free(V1);free(V2);free(V1_old);free(V2_old);
+	return *U;
+}
+
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+
+/*Calculating dual variable P (using forward differences)*/
+float DualP_2D(float *U, float *V1, float *V2, float *P1, float *P2, long dimX, long dimY, float sigma)
+{
+    long i,j, index;
+#pragma omp parallel for shared(U,V1,V2,P1,P2) private(i,j,index)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+			 index = j*dimX+i;
+            /* symmetric boundary conditions (Neuman) */
+            if (i == dimX-1) P1[index] += sigma*((U[j*dimX+(i-1)] - U[index]) - V1[index]); 
+            else P1[index] += sigma*((U[j*dimX+(i+1)] - U[index])  - V1[index]); 
+            if (j == dimY-1) P2[index] += sigma*((U[(j-1)*dimX+i] - U[index])  - V2[index]);
+            else  P2[index] += sigma*((U[(j+1)*dimX+i] - U[index])  - V2[index]);
+        }}
+    return 1;
+}
+/*Projection onto convex set for P*/
+float ProjP_2D(float *P1, float *P2, long dimX, long dimY, float alpha1)
+{
+    float grad_magn;
+    long i,j,index;
+#pragma omp parallel for shared(P1,P2) private(i,j,index,grad_magn)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+	    index = j*dimX+i;
+            grad_magn = (sqrtf(pow(P1[index],2) + pow(P2[index],2)))/alpha1;
+            if (grad_magn > 1.0f) {
+                P1[index] /= grad_magn;
+                P2[index] /= grad_magn;
+            }
+        }}
+    return 1;
+}
+/*Calculating dual variable Q (using forward differences)*/
+float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, long dimX, long dimY, float sigma)
+{
+    long i,j,index;
+    float q1, q2, q11, q22;
+#pragma omp parallel for shared(Q1,Q2,Q3,V1,V2) private(i,j,index,q1,q2,q11,q22)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+    	    index = j*dimX+i;
+    	    q1 = 0.0f; q11 = 0.0f; q2 = 0.0f; q22 = 0.0f;
+            /* boundary conditions (Neuman) */
+            if (i != dimX-1){
+                q1 = V1[j*dimX+(i+1)] - V1[index];
+                q11 = V2[j*dimX+(i+1)] - V2[index];
+            }
+            if (j != dimY-1) {
+                q2 = V2[(j+1)*dimX+i] - V2[index];
+                q22 = V1[(j+1)*dimX+i] - V1[index];
+            }
+            Q1[index] += sigma*(q1);
+            Q2[index] += sigma*(q2);
+            Q3[index] += sigma*(0.5f*(q11 + q22));
+        }}
+    return 1;
+}
+float ProjQ_2D(float *Q1, float *Q2, float *Q3, long dimX, long dimY, float alpha0)
+{
+    float grad_magn;
+    long i,j,index;
+#pragma omp parallel for shared(Q1,Q2,Q3) private(i,j,index,grad_magn)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+	   index = j*dimX+i;
+            grad_magn = sqrtf(pow(Q1[index],2) + pow(Q2[index],2) + 2*pow(Q3[index],2));
+            grad_magn = grad_magn/alpha0;
+            if (grad_magn > 1.0f) {
+                Q1[index] /= grad_magn;
+                Q2[index] /= grad_magn;
+                Q3[index] /= grad_magn;
+            }
+        }}
+    return 1;
+}
+/* Divergence and projection for P*/
+float DivProjP_2D(float *U, float *U0, float *P1, float *P2, long dimX, long dimY, float lambda, float tau)
+{
+    long i,j,index;
+    float P_v1, P_v2, div;
+#pragma omp parallel for shared(U,U0,P1,P2) private(i,j,index,P_v1,P_v2,div)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+	    index = j*dimX+i;
+            if (i == 0) P_v1 = P1[index];
+            else P_v1 = P1[index] - P1[j*dimX+(i-1)];
+            if (j == 0) P_v2 = P2[index];
+            else  P_v2 = P2[index] - P2[(j-1)*dimX+i];
+            div = P_v1 + P_v2;
+            U[index] = (lambda*(U[index] + tau*div) + tau*U0[index])/(lambda + tau);
+        }}
+    return *U;
+}
+/*get updated solution U*/
+float newU(float *U, float *U_old, long dimX, long dimY)
+{
+    long i;
+#pragma omp parallel for shared(U,U_old) private(i)
+    for(i=0; i<dimX*dimY; i++) U[i] = 2*U[i] - U_old[i];
+    return *U;
+}
+/*get update for V*/
+float UpdV_2D(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, long dimX, long dimY, float tau)
+{
+    long i, j, index;
+    float q1, q3_x, q3_y, q2, div1, div2;
+#pragma omp parallel for shared(V1,V2,P1,P2,Q1,Q2,Q3) private(i, j, index, q1, q3_x, q3_y, q2, div1, div2)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+	    index = j*dimX+i;
+              q2 = 0.0f;  q3_y = 0.0f; q1 = 0.0f; q3_x = 0.0;
+            /* boundary conditions (Neuman) */
+            if (i != 0) {
+                q1 = Q1[index] - Q1[j*dimX+(i-1)];
+                q3_x = Q3[index] - Q3[j*dimX+(i-1)];
+            }
+            if (j != 0) {
+                q2 = Q2[index] - Q2[(j-1)*dimX+i];
+                q3_y = Q3[index] - Q3[(j-1)*dimX+i];
+            }
+            div1 = q1 + q3_y;
+            div2 = q3_x + q2;
+            V1[index] += tau*(P1[index] + div1);
+            V2[index] += tau*(P2[index] + div2);
+        }}
+    return 1;
+}
+
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+/*Calculating dual variable P (using forward differences)*/
+float DualP_3D(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float sigma)
+{
+    long i,j,k, index;
+#pragma omp parallel for shared(U,V1,V2,V3,P1,P2,P3) private(i,j,k,index)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+          for(k=0; k<dimZ; k++) {             	   
+    	   index = (dimX*dimY)*k + j*dimX+i;    	   
+            /* symmetric boundary conditions (Neuman) */
+            if (i == dimX-1) P1[index] += sigma*((U[(dimX*dimY)*k + j*dimX+(i-1)] - U[index]) - V1[index]); 
+            else P1[index] += sigma*((U[(dimX*dimY)*k + j*dimX+(i+1)] - U[index])  - V1[index]); 
+            if (j == dimY-1) P2[index] += sigma*((U[(dimX*dimY)*k + (j-1)*dimX+i] - U[index])  - V2[index]);
+            else  P2[index] += sigma*((U[(dimX*dimY)*k + (j+1)*dimX+i] - U[index])  - V2[index]);
+            if (k == dimZ-1) P3[index] += sigma*((U[(dimX*dimY)*(k-1) + j*dimX+i] - U[index])  - V3[index]);
+            else  P3[index] += sigma*((U[(dimX*dimY)*(k+1) + j*dimX+i] - U[index])  - V3[index]);
+        }}}
+    return 1;
+}
+/*Projection onto convex set for P*/
+float ProjP_3D(float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float alpha1)
+{
+    float grad_magn;
+    long i,j,k,index;
+#pragma omp parallel for shared(P1,P2,P3) private(i,j,k,index,grad_magn)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+	  for(k=0; k<dimZ; k++) {   	
+   	    index = (dimX*dimY)*k + j*dimX+i;
+            grad_magn = (sqrtf(pow(P1[index],2) + pow(P2[index],2) + pow(P3[index],2)))/alpha1;
+            if (grad_magn > 1.0f) {
+                P1[index] /= grad_magn;
+                P2[index] /= grad_magn;
+                P3[index] /= grad_magn;
+            }
+        }}}
+    return 1;
+}
+/*Calculating dual variable Q (using forward differences)*/
+float DualQ_3D(float *V1, float *V2, float *V3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float sigma)
+{
+    long i,j,k,index;
+    float q1, q2, q3, q11, q22, q33, q44, q55, q66;
+#pragma omp parallel for shared(Q1,Q2,Q3,Q4,Q5,Q6,V1,V2,V3) private(i,j,k,index,q1,q2,q3,q11,q22,q33,q44,q55,q66)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+       	  for(k=0; k<dimZ; k++) {   	
+	    index = (dimX*dimY)*k + j*dimX+i;
+	    q1 = 0.0f; q11 = 0.0f; q33 = 0.0f; q2 = 0.0f; q22 = 0.0f; q55 = 0.0f; q3 = 0.0f; q44 = 0.0f; q66 = 0.0f;
+            /* symmetric boundary conditions (Neuman) */
+            if (i != dimX-1){ 
+                q1 = V1[(dimX*dimY)*k + j*dimX+(i+1)] - V1[index];              
+                q11 = V2[(dimX*dimY)*k + j*dimX+(i+1)] - V2[index];
+                q33 = V3[(dimX*dimY)*k + j*dimX+(i+1)] - V3[index];
+            }
+            if (j != dimY-1) {
+                q2 = V2[(dimX*dimY)*k + (j+1)*dimX+i] - V2[index];                
+                q22 = V1[(dimX*dimY)*k + (j+1)*dimX+i] - V1[index];
+                q55 = V3[(dimX*dimY)*k + (j+1)*dimX+i] - V3[index];
+            }
+            if (k != dimZ-1) {
+                q3 = V3[(dimX*dimY)*(k+1) + j*dimX+i] - V3[index];
+                q44 = V1[(dimX*dimY)*(k+1) + j*dimX+i] - V1[index];
+                q66 = V2[(dimX*dimY)*(k+1) + j*dimX+i] - V2[index];
+            }
+            
+            Q1[index] += sigma*(q1); /*Q11*/
+            Q2[index] += sigma*(q2); /*Q22*/            
+            Q3[index] += sigma*(q3); /*Q33*/
+            Q4[index] += sigma*(0.5f*(q11 + q22)); /* Q21 / Q12 */
+            Q5[index] += sigma*(0.5f*(q33 + q44)); /* Q31 / Q13 */
+            Q6[index] += sigma*(0.5f*(q55 + q66)); /* Q32 / Q23 */
+        }}}
+    return 1;
+}
+float ProjQ_3D(float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float alpha0)
+{
+    float grad_magn;
+    long i,j,k,index;
+#pragma omp parallel for shared(Q1,Q2,Q3,Q4,Q5,Q6) private(i,j,k,index,grad_magn)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+       	  for(k=0; k<dimZ; k++) {   	
+	    index = (dimX*dimY)*k + j*dimX+i;           
+            grad_magn = sqrtf(pow(Q1[index],2) + pow(Q2[index],2) + pow(Q3[index],2) + 2.0f*pow(Q4[index],2) + 2.0f*pow(Q5[index],2) + 2.0f*pow(Q6[index],2));
+            grad_magn = grad_magn/alpha0;
+            if (grad_magn > 1.0f) {
+                Q1[index] /= grad_magn;
+                Q2[index] /= grad_magn;
+                Q3[index] /= grad_magn;
+                Q4[index] /= grad_magn;
+                Q5[index] /= grad_magn;
+                Q6[index] /= grad_magn;
+            }
+        }}}
+    return 1;
+}
+/* Divergence and projection for P*/
+float DivProjP_3D(float *U, float *U0, float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float lambda, float tau)
+{
+    long i,j,k,index;
+    float P_v1, P_v2, P_v3, div;
+#pragma omp parallel for shared(U,U0,P1,P2,P3) private(i,j,k,index,P_v1,P_v2,P_v3,div)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+       	  for(k=0; k<dimZ; k++) {   	
+	    index = (dimX*dimY)*k + j*dimX+i; 	    
+            if (i == 0) P_v1 = P1[index];
+            else P_v1 = P1[index] - P1[(dimX*dimY)*k + j*dimX+(i-1)];
+            if (j == 0) P_v2 = P2[index];
+            else P_v2 = P2[index] - P2[(dimX*dimY)*k + (j-1)*dimX+i];
+            if (k == 0) P_v3 = P3[index];
+            else P_v3 = P3[index] - P3[(dimX*dimY)*(k-1) + (j)*dimX+i];              
+                      
+            div = P_v1 + P_v2 + P_v3;
+            U[index] = (lambda*(U[index] + tau*div) + tau*U0[index])/(lambda + tau); 
+        }}}
+    return *U;
+}
+/*get update for V*/
+float UpdV_3D(float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float tau)
+{
+    long i,j,k,index;
+    float q1, q4x, q5x, q2, q4y, q6y, q6z, q5z, q3, div1, div2, div3;
+#pragma omp parallel for shared(V1,V2,V3,P1,P2,P3,Q1,Q2,Q3,Q4,Q5,Q6) private(i,j,k,index,q1,q4x,q5x,q2,q4y,q6y,q6z,q5z,q3,div1,div2,div3)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+       	  for(k=0; k<dimZ; k++) {   	
+	    index = (dimX*dimY)*k + j*dimX+i; 	
+	    q1 = 0.0f; q4x= 0.0f; q5x= 0.0f; q2= 0.0f; q4y= 0.0f; q6y= 0.0f; q6z= 0.0f; q5z= 0.0f; q3= 0.0f;
+            /* Q1 - Q11, Q2 - Q22, Q3 -  Q33, Q4 - Q21/Q12, Q5 - Q31/Q13, Q6 - Q32/Q23*/            
+            /* symmetric boundary conditions (Neuman) */
+            if (i != 0) {
+                q1 = Q1[index] - Q1[(dimX*dimY)*k + j*dimX+(i-1)];
+                q4x = Q4[index] - Q4[(dimX*dimY)*k + j*dimX+(i-1)];                
+                q5x = Q5[index] - Q5[(dimX*dimY)*k + j*dimX+(i-1)];
+            }
+            if (j != 0) {
+                q2 = Q2[index] - Q2[(dimX*dimY)*k + (j-1)*dimX+i];
+                q4y = Q4[index] - Q4[(dimX*dimY)*k + (j-1)*dimX+i];
+                q6y = Q6[index] - Q6[(dimX*dimY)*k + (j-1)*dimX+i];
+            }
+             if (k != 0) {
+                q6z = Q6[index] - Q6[(dimX*dimY)*(k-1) + (j)*dimX+i];
+                q5z = Q5[index] - Q5[(dimX*dimY)*(k-1) + (j)*dimX+i];
+                q3 = Q3[index] - Q3[(dimX*dimY)*(k-1) + (j)*dimX+i];
+            }
+            div1 = q1 + q4y + q5z;
+            div2 = q4x + q2 + q6z;            
+            div3 = q5x + q6y + q3;
+            
+            V1[index] += tau*(P1[index] + div1);
+            V2[index] += tau*(P2[index] + div2);
+            V3[index] += tau*(P3[index] + div3);
+        }}}
+    return 1;
+}
+
+float copyIm_3Ar(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, long dimX, long dimY, long dimZ)
+{
+	long j;
+#pragma omp parallel for shared(V1, V2, V3, V1_old, V2_old, V3_old) private(j)
+	for (j = 0; j<dimX*dimY*dimZ; j++)  {	
+	V1_old[j] = V1[j];
+	V2_old[j] = V2[j];
+	V3_old[j] = V3[j];	
+	}
+	return 1;
+}
+
+/*get updated solution U*/
+float newU3D(float *U, float *U_old, long dimX, long dimY, long dimZ)
+{
+    long i;
+#pragma omp parallel for shared(U, U_old) private(i)
+    for(i=0; i<dimX*dimY*dimZ; i++) U[i] = 2.0f*U[i] - U_old[i];
+    return *U;
+}
+
+
+/*get updated solution U*/
+float newU3D_3Ar(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, long dimX, long dimY, long dimZ)
+{
+    long i;
+#pragma omp parallel for shared(V1, V2, V3, V1_old, V2_old, V3_old) private(i)
+    for(i=0; i<dimX*dimY*dimZ; i++) {
+    V1[i] = 2.0f*V1[i] - V1_old[i];
+    V2[i] = 2.0f*V2[i] - V2_old[i];
+    V3[i] = 2.0f*V3[i] - V3_old[i];
+    }
+    return 1;
+}
+
diff --git a/src/Core/regularisers_CPU/TGV_core.h b/src/Core/regularisers_CPU/TGV_core.h
new file mode 100644
index 0000000..11b12c1
--- /dev/null
+++ b/src/Core/regularisers_CPU/TGV_core.h
@@ -0,0 +1,73 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+/* C-OMP implementation of Primal-Dual denoising method for 
+ * Total Generilized Variation (TGV)-L2 model [1] (2D/3D)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume (2D/3D)
+ * 2. lambda - regularisation parameter
+ * 3. parameter to control the first-order term (alpha1)
+ * 4. parameter to control the second-order term (alpha0)
+ * 5. Number of Chambolle-Pock (Primal-Dual) iterations
+ * 6. Lipshitz constant (default is 12)
+ * 
+ * Output:
+ * Filtered/regularised image/volume
+ *
+ * References:
+ * [1] K. Bredies "Total Generalized Variation"
+ */
+ 
+ 
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+CCPI_EXPORT float TGV_main(float *U0, float *U, float lambda, float alpha1, float alpha0, int iter, float L2, int dimX, int dimY, int dimZ);
+
+/* 2D functions */
+CCPI_EXPORT float DualP_2D(float *U, float *V1, float *V2, float *P1, float *P2, long dimX, long dimY, float sigma);
+CCPI_EXPORT float ProjP_2D(float *P1, float *P2, long dimX, long dimY, float alpha1);
+CCPI_EXPORT float DualQ_2D(float *V1, float *V2, float *Q1, float *Q2, float *Q3, long dimX, long dimY, float sigma);
+CCPI_EXPORT float ProjQ_2D(float *Q1, float *Q2, float *Q3, long dimX, long dimY, float alpha0);
+CCPI_EXPORT float DivProjP_2D(float *U, float *U0, float *P1, float *P2, long dimX, long dimY, float lambda, float tau);
+CCPI_EXPORT float UpdV_2D(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, long dimX, long dimY, float tau);
+CCPI_EXPORT float newU(float *U, float *U_old, long dimX, long dimY);
+/* 3D functions */
+CCPI_EXPORT float DualP_3D(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float sigma);
+CCPI_EXPORT float ProjP_3D(float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float alpha1);
+CCPI_EXPORT float DualQ_3D(float *V1, float *V2, float *V3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float sigma);
+CCPI_EXPORT float ProjQ_3D(float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float alpha0);
+CCPI_EXPORT float DivProjP_3D(float *U, float *U0, float *P1, float *P2, float *P3, long dimX, long dimY, long dimZ, float lambda, float tau);
+CCPI_EXPORT float UpdV_3D(float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, long dimX, long dimY, long dimZ, float tau);
+CCPI_EXPORT float newU3D(float *U, float *U_old, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float copyIm_3Ar(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float newU3D_3Ar(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_CPU/TNV_core.c b/src/Core/regularisers_CPU/TNV_core.c
new file mode 100755
index 0000000..753cc5f
--- /dev/null
+++ b/src/Core/regularisers_CPU/TNV_core.c
@@ -0,0 +1,452 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "TNV_core.h"
+
+/*
+ * C-OMP implementation of Total Nuclear Variation regularisation model (2D + channels) [1]
+ * The code is modified from the implementation by Joan Duran <joan.duran@uib.es> see
+ * "denoisingPDHG_ipol.cpp" in Joans Collaborative Total Variation package
+ *
+ * Input Parameters:
+ * 1. Noisy volume of 2D + channel dimension, i.e. 3D volume
+ * 2. lambda - regularisation parameter
+ * 3. Number of iterations [OPTIONAL parameter]
+ * 4. eplsilon - tolerance constant [OPTIONAL parameter]
+ * 5. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+ *
+ * Output:
+ * 1. Filtered/regularized image
+ *
+ * [1]. Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.
+ */
+
+float TNV_CPU_main(float *Input, float *u, float lambda, int maxIter, float tol, int dimX, int dimY, int dimZ)
+{
+    long k, p, q, r, DimTotal;
+    float taulambda;
+    float *u_upd, *gx, *gy, *gx_upd, *gy_upd, *qx, *qy, *qx_upd, *qy_upd, *v, *vx, *vy, *gradx, *grady, *gradx_upd, *grady_upd, *gradx_ubar, *grady_ubar, *div, *div_upd;
+    
+    p = 1l;
+    q = 1l;
+    r = 0l;
+    
+    lambda = 1.0f/(2.0f*lambda);
+    DimTotal = (long)(dimX*dimY*dimZ);
+    /* PDHG algorithm parameters*/
+    float tau = 0.5f;
+    float sigma = 0.5f;
+    float theta = 1.0f;
+    
+    // Auxiliar vectors
+    u_upd = calloc(DimTotal, sizeof(float));
+    gx = calloc(DimTotal, sizeof(float));
+    gy = calloc(DimTotal, sizeof(float));
+    gx_upd = calloc(DimTotal, sizeof(float));
+    gy_upd = calloc(DimTotal, sizeof(float));
+    qx = calloc(DimTotal, sizeof(float));
+    qy = calloc(DimTotal, sizeof(float));
+    qx_upd = calloc(DimTotal, sizeof(float));
+    qy_upd = calloc(DimTotal, sizeof(float));
+    v = calloc(DimTotal, sizeof(float));
+    vx = calloc(DimTotal, sizeof(float));
+    vy = calloc(DimTotal, sizeof(float));
+    gradx = calloc(DimTotal, sizeof(float));
+    grady = calloc(DimTotal, sizeof(float));
+    gradx_upd = calloc(DimTotal, sizeof(float));
+    grady_upd = calloc(DimTotal, sizeof(float));
+    gradx_ubar = calloc(DimTotal, sizeof(float));
+    grady_ubar = calloc(DimTotal, sizeof(float));
+    div = calloc(DimTotal, sizeof(float));
+    div_upd = calloc(DimTotal, sizeof(float));
+    
+    // Backtracking parameters
+    float s = 1.0f;
+    float gamma = 0.75f;
+    float beta = 0.95f;
+    float alpha0 = 0.2f;
+    float alpha = alpha0;
+    float delta = 1.5f;
+    float eta = 0.95f;
+    
+    // PDHG algorithm parameters
+    taulambda = tau * lambda;
+    float divtau = 1.0f / tau;
+    float divsigma = 1.0f / sigma;
+    float theta1 = 1.0f + theta;
+    
+    /*allocate memory for  taulambda */
+    //taulambda = (float*) calloc(dimZ, sizeof(float));
+    //for(k=0; k < dimZ; k++)  {taulambda[k] = tau*lambda[k];}
+    
+    // Apply Primal-Dual Hybrid Gradient scheme
+    int iter = 0;
+    float residual = fLarge;
+    float ubarx, ubary;
+    
+    for(iter = 0; iter < maxIter; iter++)   {
+        // Argument of proximal mapping of fidelity term
+#pragma omp parallel for shared(v, u) private(k)
+        for(k=0; k<dimX*dimY*dimZ; k++)  {v[k] = u[k] + tau*div[k];}
+
+// Proximal solution of fidelity term
+proxG(u_upd, v, Input, taulambda, (long)(dimX), (long)(dimY), (long)(dimZ));
+
+// Gradient of updated primal variable
+gradient(u_upd, gradx_upd, grady_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+
+// Argument of proximal mapping of regularization term
+#pragma omp parallel for shared(gradx_upd, grady_upd, gradx, grady) private(k, ubarx, ubary)
+for(k=0; k<dimX*dimY*dimZ; k++) {
+    ubarx = theta1 * gradx_upd[k] - theta * gradx[k];
+    ubary = theta1 * grady_upd[k] - theta * grady[k];
+    vx[k] = ubarx + divsigma * qx[k];
+    vy[k] = ubary + divsigma * qy[k];
+    gradx_ubar[k] = ubarx;
+    grady_ubar[k] = ubary;
+}
+
+proxF(gx_upd, gy_upd, vx, vy, sigma, p, q, r, (long)(dimX), (long)(dimY), (long)(dimZ));
+
+// Update dual variable
+#pragma omp parallel for shared(qx_upd, qy_upd) private(k)
+for(k=0; k<dimX*dimY*dimZ; k++) {
+    qx_upd[k] = qx[k] + sigma * (gradx_ubar[k] - gx_upd[k]);
+    qy_upd[k] = qy[k] + sigma * (grady_ubar[k] - gy_upd[k]);
+}
+
+// Divergence of updated dual variable
+#pragma omp parallel for shared(div_upd) private(k)
+for(k=0; k<dimX*dimY*dimZ; k++)  {div_upd[k] = 0.0f;}
+divergence(qx_upd, qy_upd, div_upd, dimX, dimY, dimZ);
+
+// Compute primal residual, dual residual, and backtracking condition
+float resprimal = 0.0f;
+float resdual = 0.0f;
+float product = 0.0f;
+float unorm = 0.0f;
+float qnorm = 0.0f;
+
+for(k=0; k<dimX*dimY*dimZ; k++) {
+    float udiff = u[k] - u_upd[k];
+    float qxdiff = qx[k] - qx_upd[k];
+    float qydiff = qy[k] - qy_upd[k];
+    float divdiff = div[k] - div_upd[k];
+    float gradxdiff = gradx[k] - gradx_upd[k];
+    float gradydiff = grady[k] - grady_upd[k];
+    
+    resprimal += fabs(divtau*udiff + divdiff);
+    resdual += fabs(divsigma*qxdiff - gradxdiff);
+    resdual += fabs(divsigma*qydiff - gradydiff);
+    
+    unorm += (udiff * udiff);
+    qnorm += (qxdiff * qxdiff + qydiff * qydiff);
+    product += (gradxdiff * qxdiff + gradydiff * qydiff);
+}
+
+float b = (2.0f * tau * sigma * product) / (gamma * sigma * unorm +
+        gamma * tau * qnorm);
+
+// Adapt step-size parameters
+float dual_dot_delta = resdual * s * delta;
+float dual_div_delta = (resdual * s) / delta;
+
+if(b > 1)
+{
+    // Decrease step-sizes to fit balancing principle
+    tau = (beta * tau) / b;
+    sigma = (beta * sigma) / b;
+    alpha = alpha0;
+    
+    copyIm(u, u_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    copyIm(gx, gx_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    copyIm(gy, gy_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    copyIm(qx, qx_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    copyIm(qy, qy_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    copyIm(gradx, gradx_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    copyIm(grady, grady_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    copyIm(div, div_upd, (long)(dimX), (long)(dimY), (long)(dimZ));
+    
+} else if(resprimal > dual_dot_delta)
+{
+    // Increase primal step-size and decrease dual step-size
+    tau = tau / (1.0f - alpha);
+    sigma = sigma * (1.0f - alpha);
+    alpha = alpha * eta;
+    
+} else if(resprimal < dual_div_delta)
+{
+    // Decrease primal step-size and increase dual step-size
+    tau = tau * (1.0f - alpha);
+    sigma = sigma / (1.0f - alpha);
+    alpha = alpha * eta;
+}
+
+// Update variables
+taulambda = tau * lambda;
+//for(k=0; k < dimZ; k++) taulambda[k] = tau*lambda[k];
+
+divsigma = 1.0f / sigma;
+divtau = 1.0f / tau;
+
+copyIm(u_upd, u, (long)(dimX), (long)(dimY), (long)(dimZ));
+copyIm(gx_upd, gx, (long)(dimX), (long)(dimY), (long)(dimZ));
+copyIm(gy_upd, gy, (long)(dimX), (long)(dimY), (long)(dimZ));
+copyIm(qx_upd, qx, (long)(dimX), (long)(dimY), (long)(dimZ));
+copyIm(qy_upd, qy, (long)(dimX), (long)(dimY), (long)(dimZ));
+copyIm(gradx_upd, gradx, (long)(dimX), (long)(dimY), (long)(dimZ));
+copyIm(grady_upd, grady, (long)(dimX), (long)(dimY), (long)(dimZ));
+copyIm(div_upd, div, (long)(dimX), (long)(dimY), (long)(dimZ));
+
+// Compute residual at current iteration
+residual = (resprimal + resdual) / ((float) (dimX*dimY*dimZ));
+
+//       printf("%f \n", residual);
+if (residual < tol) {
+    printf("Iterations stopped at %i with the residual %f \n", iter, residual);
+    break; }
+
+    }
+    printf("Iterations stopped at %i with the residual %f \n", iter, residual);
+    free (u_upd); free(gx); free(gy); free(gx_upd); free(gy_upd);
+    free(qx); free(qy); free(qx_upd); free(qy_upd); free(v); free(vx); free(vy);
+    free(gradx); free(grady); free(gradx_upd); free(grady_upd); free(gradx_ubar);
+    free(grady_ubar); free(div); free(div_upd);    
+    return *u;
+}
+
+float proxG(float *u_upd, float *v, float *f, float taulambda, long dimX, long dimY, long dimZ)
+{
+    float constant;
+    long k;
+    constant = 1.0f + taulambda;
+#pragma omp parallel for shared(v, f, u_upd) private(k)
+    for(k=0; k<dimZ*dimX*dimY; k++) {
+        u_upd[k] = (v[k] + taulambda * f[k])/constant;
+        //u_upd[(dimX*dimY)*k + l] = (v[(dimX*dimY)*k + l] + taulambda * f[(dimX*dimY)*k + l])/constant;
+    }
+    return *u_upd;
+}
+
+float gradient(float *u_upd, float *gradx_upd, float *grady_upd, long dimX, long dimY, long dimZ)
+{
+    long i, j, k, l;
+    // Compute discrete gradient using forward differences
+#pragma omp parallel for shared(gradx_upd,grady_upd,u_upd) private(i, j, k, l)
+    for(k = 0; k < dimZ; k++)   {
+        for(j = 0; j < dimY; j++)   {
+            l = j * dimX;           
+            for(i = 0; i < dimX; i++)   {
+                // Derivatives in the x-direction
+                if(i != dimX-1)
+                    gradx_upd[(dimX*dimY)*k + i+l] = u_upd[(dimX*dimY)*k + i+1+l] - u_upd[(dimX*dimY)*k + i+l];
+                else
+                    gradx_upd[(dimX*dimY)*k + i+l] = 0.0f;
+                
+                // Derivatives in the y-direction
+                if(j != dimY-1)
+                    //grady_upd[(dimX*dimY)*k + i+l] = u_upd[(dimX*dimY)*k + i+dimY+l] -u_upd[(dimX*dimY)*k + i+l];
+                    grady_upd[(dimX*dimY)*k + i+l] = u_upd[(dimX*dimY)*k + i+(j+1)*dimX] -u_upd[(dimX*dimY)*k + i+l];
+                else
+                    grady_upd[(dimX*dimY)*k + i+l] = 0.0f;
+            }}}
+    return 1;
+}
+
+float proxF(float *gx, float *gy, float *vx, float *vy, float sigma, int p, int q, int r, long dimX, long dimY, long dimZ)
+{
+    // (S^p, \ell^1) norm decouples at each pixel
+//   Spl1(gx, gy, vx, vy, sigma, p, num_channels, dim);
+    float divsigma = 1.0f / sigma;
+    
+    // $\ell^{1,1,1}$-TV regularization
+//       int i,j,k;
+//     #pragma omp parallel for shared (gx,gy,vx,vy) private(i,j,k)
+//      for(k = 0; k < dimZ; k++)  {
+//         for(i=0; i<dimX; i++) {
+//              for(j=0; j<dimY; j++) {
+//                 gx[(dimX*dimY)*k + (i)*dimY + (j)] = SIGN(vx[(dimX*dimY)*k + (i)*dimY + (j)]) * MAX(fabs(vx[(dimX*dimY)*k + (i)*dimY + (j)]) - divsigma,  0.0f);
+//                 gy[(dimX*dimY)*k + (i)*dimY + (j)] = SIGN(vy[(dimX*dimY)*k + (i)*dimY + (j)]) * MAX(fabs(vy[(dimX*dimY)*k + (i)*dimY + (j)]) - divsigma,  0.0f);
+//             }}}
+    
+    // Auxiliar vector
+    float *proj, sum, shrinkfactor ;
+    float M1,M2,M3,valuex,valuey,T,D,det,eig1,eig2,sig1,sig2,V1, V2, V3, V4, v0,v1,v2, mu1,mu2,sig1_upd,sig2_upd,t1,t2,t3;
+    long i,j,k, ii, num;
+#pragma omp parallel for shared (gx,gy,vx,vy,p) private(i,ii,j,k,proj,num, sum, shrinkfactor, M1,M2,M3,valuex,valuey,T,D,det,eig1,eig2,sig1,sig2,V1, V2, V3, V4,v0,v1,v2,mu1,mu2,sig1_upd,sig2_upd,t1,t2,t3)
+    for(i=0; i<dimX; i++) {
+        for(j=0; j<dimY; j++) {
+            
+            proj = (float*) calloc (2,sizeof(float));
+            // Compute matrix $M\in\R^{2\times 2}$
+            M1 = 0.0f;
+            M2 = 0.0f;
+            M3 = 0.0f;
+            
+            for(k = 0; k < dimZ; k++)
+            {
+                valuex = vx[(dimX*dimY)*k + (j)*dimX + (i)];
+                valuey = vy[(dimX*dimY)*k + (j)*dimX + (i)];
+                
+                M1 += (valuex * valuex);
+                M2 += (valuex * valuey);
+                M3 += (valuey * valuey);
+            }
+            
+            // Compute eigenvalues of M
+            T = M1 + M3;
+            D = M1 * M3 - M2 * M2;
+            det = sqrt(MAX((T * T / 4.0f) - D, 0.0f));
+            eig1 = MAX((T / 2.0f) + det, 0.0f);
+            eig2 = MAX((T / 2.0f) - det, 0.0f);
+            sig1 = sqrt(eig1);
+            sig2 = sqrt(eig2);
+            
+            // Compute normalized eigenvectors
+            V1 = V2 = V3 = V4 = 0.0f;
+            
+            if(M2 != 0.0f)
+            {
+                v0 = M2;
+                v1 = eig1 - M3;
+                v2 = eig2 - M3;
+                
+                mu1 = sqrtf(v0 * v0 + v1 * v1);
+                mu2 = sqrtf(v0 * v0 + v2 * v2);
+                
+                if(mu1 > fTiny)
+                {
+                    V1 = v1 / mu1;
+                    V3 = v0 / mu1;
+                }
+                
+                if(mu2 > fTiny)
+                {
+                    V2 = v2 / mu2;
+                    V4 = v0 / mu2;
+                }
+                
+            } else
+            {
+                if(M1 > M3)
+                {
+                    V1 = V4 = 1.0f;
+                    V2 = V3 = 0.0f;
+                    
+                } else
+                {
+                    V1 = V4 = 0.0f;
+                    V2 = V3 = 1.0f;
+                }
+            }
+            
+            // Compute prox_p of the diagonal entries
+            sig1_upd = sig2_upd = 0.0f;
+            
+            if(p == 1)
+            {
+                sig1_upd = MAX(sig1 - divsigma, 0.0f);
+                sig2_upd = MAX(sig2 - divsigma, 0.0f);
+                
+            } else if(p == INFNORM)
+            {
+                proj[0] = sigma * fabs(sig1);
+                proj[1] = sigma * fabs(sig2);
+                
+                /*l1 projection part */
+                sum = fLarge;
+                num = 0l;
+                shrinkfactor = 0.0f;
+                while(sum > 1.0f)
+                {
+                    sum = 0.0f;
+                    num = 0;
+                    
+                    for(ii = 0; ii < 2; ii++)
+                    {
+                        proj[ii] = MAX(proj[ii] - shrinkfactor, 0.0f);
+                        
+                        sum += fabs(proj[ii]);
+                        if(proj[ii]!= 0.0f)
+                            num++;
+                    }
+                    
+                    if(num > 0)
+                        shrinkfactor = (sum - 1.0f) / num;
+                    else
+                        break;
+                }
+                /*l1 proj ends*/
+                
+                sig1_upd = sig1 - divsigma * proj[0];
+                sig2_upd = sig2 - divsigma * proj[1];
+            }
+            
+            // Compute the diagonal entries of $\widehat{\Sigma}\Sigma^{\dagger}_0$
+            if(sig1 > fTiny)
+                sig1_upd /= sig1;
+            
+            if(sig2 > fTiny)
+                sig2_upd /= sig2;
+            
+            // Compute solution
+            t1 = sig1_upd * V1 * V1 + sig2_upd * V2 * V2;
+            t2 = sig1_upd * V1 * V3 + sig2_upd * V2 * V4;
+            t3 = sig1_upd * V3 * V3 + sig2_upd * V4 * V4;
+            
+            for(k = 0; k < dimZ; k++)
+            {
+                gx[(dimX*dimY)*k + j*dimX + i] = vx[(dimX*dimY)*k + j*dimX + i] * t1 + vy[(dimX*dimY)*k + j*dimX + i] * t2;
+                gy[(dimX*dimY)*k + j*dimX + i] = vx[(dimX*dimY)*k + j*dimX + i] * t2 + vy[(dimX*dimY)*k + j*dimX + i] * t3;
+            }           
+            
+            // Delete allocated memory
+            free(proj);
+        }}
+    
+    return 1;
+}
+
+float divergence(float *qx_upd, float *qy_upd, float *div_upd, long dimX, long dimY, long dimZ)
+{
+    long i, j, k, l;
+#pragma omp parallel for shared(qx_upd,qy_upd,div_upd) private(i, j, k, l)
+    for(k = 0; k < dimZ; k++)   {
+        for(j = 0; j < dimY; j++)   {
+            l = j * dimX;            
+            for(i = 0; i < dimX; i++)   {
+                if(i != dimX-1)
+                {
+                    // ux[k][i+l] = u[k][i+1+l] - u[k][i+l]
+                    div_upd[(dimX*dimY)*k + i+1+l] -= qx_upd[(dimX*dimY)*k + i+l];
+                    div_upd[(dimX*dimY)*k + i+l] += qx_upd[(dimX*dimY)*k + i+l];
+                }
+                
+                if(j != dimY-1)
+                {
+                    // uy[k][i+l] = u[k][i+width+l] - u[k][i+l]
+                    //div_upd[(dimX*dimY)*k + i+dimY+l] -= qy_upd[(dimX*dimY)*k + i+l];
+                    div_upd[(dimX*dimY)*k + i+(j+1)*dimX] -= qy_upd[(dimX*dimY)*k + i+l];                    
+                    div_upd[(dimX*dimY)*k + i+l] += qy_upd[(dimX*dimY)*k + i+l];
+                }
+            }
+        }
+    }
+    return *div_upd;
+}
diff --git a/src/Core/regularisers_CPU/TNV_core.h b/src/Core/regularisers_CPU/TNV_core.h
new file mode 100644
index 0000000..aa050a4
--- /dev/null
+++ b/src/Core/regularisers_CPU/TNV_core.h
@@ -0,0 +1,47 @@
+#include <math.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <stdio.h>
+#include "omp.h"
+#include "utils.h"
+#include "CCPiDefines.h"
+
+#define fTiny 0.00000001f
+#define fLarge 100000000.0f
+#define INFNORM -1
+
+#define MAX(i,j) ((i)<(j) ? (j):(i))
+#define MIN(i,j) ((i)<(j) ? (i):(j))
+
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float TNV_CPU_main(float *Input, float *u, float lambda, int maxIter, float tol, int dimX, int dimY, int dimZ);
+
+/*float PDHG(float *A, float *B, float tau, float sigma, float theta, float lambda, int p, int q, int r, float tol, int maxIter, int d_c, int d_w, int d_h);*/
+CCPI_EXPORT float proxG(float *u_upd, float *v, float *f, float taulambda, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float gradient(float *u_upd, float *gradx_upd, float *grady_upd, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float proxF(float *gx, float *gy, float *vx, float *vy, float sigma, int p, int q, int r, long dimX, long dimY, long dimZ);
+CCPI_EXPORT float divergence(float *qx_upd, float *qy_upd, float *div_upd, long dimX, long dimY, long dimZ);
+#ifdef __cplusplus
+}
+#endif
\ No newline at end of file
diff --git a/src/Core/regularisers_CPU/utils.c b/src/Core/regularisers_CPU/utils.c
new file mode 100644
index 0000000..7a4e80b
--- /dev/null
+++ b/src/Core/regularisers_CPU/utils.c
@@ -0,0 +1,117 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazanteev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "utils.h"
+#include <math.h>
+
+/* Copy Image (float) */
+float copyIm(float *A, float *U, long dimX, long dimY, long dimZ)
+{
+	long j;
+#pragma omp parallel for shared(A, U) private(j)
+	for (j = 0; j<dimX*dimY*dimZ; j++)  U[j] = A[j];
+	return *U;
+}
+
+/* Copy Image */
+unsigned char copyIm_unchar(unsigned char *A, unsigned char *U, int dimX, int dimY, int dimZ)
+{
+	int j;
+#pragma omp parallel for shared(A, U) private(j)
+	for (j = 0; j<dimX*dimY*dimZ; j++)  U[j] = A[j];
+	return *U;
+}
+
+/*Roll image symmetrically from top to bottom*/
+float copyIm_roll(float *A, float *U, int dimX, int dimY, int roll_value, int switcher)
+{
+    int i, j;
+#pragma omp parallel for shared(U, A) private(i,j)
+    for (i=0; i<dimX; i++) {
+        for (j=0; j<dimY; j++) {
+            if (switcher == 0) {
+                if (j < (dimY - roll_value)) U[j*dimX + i] = A[(j+roll_value)*dimX + i];
+                else U[j*dimX + i] = A[(j - (dimY - roll_value))*dimX + i];
+            }
+            else {
+                if (j < roll_value) U[j*dimX + i] = A[(j+(dimY - roll_value))*dimX + i];
+                else U[j*dimX + i] = A[(j - roll_value)*dimX + i];
+            }
+        }}
+    return *U;
+}
+
+/* function that calculates TV energy
+ * type - 1:  2*lambda*min||\nabla u|| + ||u -u0||^2
+ * type - 2:  2*lambda*min||\nabla u|| 
+ * */
+float TV_energy2D(float *U, float *U0, float *E_val, float lambda, int type, int dimX, int dimY)
+{
+	int i, j, i1, j1, index;
+	float NOMx_2, NOMy_2, E_Grad=0.0f, E_Data=0.0f;
+	
+	/* first calculate \grad U_xy*/	
+        for(j=0; j<dimY; j++) {
+            for(i=0; i<dimX; i++) {
+				index = j*dimX+i;
+                /* boundary conditions */
+                i1 = i + 1; if (i == dimX-1) i1 = i;
+                j1 = j + 1; if (j == dimY-1) j1 = j;
+                
+                /* Forward differences */                
+                NOMx_2 = powf((float)(U[j1*dimX + i] - U[index]),2); /* x+ */
+                NOMy_2 = powf((float)(U[j*dimX + i1] - U[index]),2); /* y+ */
+                E_Grad += 2.0f*lambda*sqrtf((float)(NOMx_2) + (float)(NOMy_2)); /* gradient term energy */
+                E_Data += powf((float)(U[index]-U0[index]),2); /* fidelity term energy */
+			}
+		}
+		if (type == 1) E_val[0] = E_Grad + E_Data;
+		if (type == 2) E_val[0] = E_Grad;
+		return *E_val;
+}
+
+float TV_energy3D(float *U, float *U0, float *E_val, float lambda, int type, int dimX, int dimY, int dimZ)
+{
+	long i, j, k, i1, j1, k1, index;
+	float NOMx_2, NOMy_2, NOMz_2, E_Grad=0.0f, E_Data=0.0f;
+	
+	/* first calculate \grad U_xy*/	
+    for(j=0; j<(long)(dimY); j++) {
+        for(i=0; i<(long)(dimX); i++) {
+            for(k=0; k<(long)(dimZ); k++) {
+				index = (dimX*dimY)*k + j*dimX+i;
+                /* boundary conditions */
+                i1 = i + 1; if (i == (long)(dimX-1)) i1 = i;
+                j1 = j + 1; if (j == (long)(dimY-1)) j1 = j;
+                k1 = k + 1; if (k == (long)(dimZ-1)) k1 = k;
+                
+                /* Forward differences */                
+                NOMx_2 = powf((float)(U[(dimX*dimY)*k + j1*dimX+i] - U[index]),2); /* x+ */
+                NOMy_2 = powf((float)(U[(dimX*dimY)*k + j*dimX+i1] - U[index]),2); /* y+ */
+                NOMz_2 = powf((float)(U[(dimX*dimY)*k1 + j*dimX+i] - U[index]),2); /* z+ */
+                
+                E_Grad += 2.0f*lambda*sqrtf((float)(NOMx_2) + (float)(NOMy_2) + (float)(NOMz_2)); /* gradient term energy */
+                E_Data += (powf((float)(U[index]-U0[index]),2)); /* fidelity term energy */
+			}
+		}
+	}
+		if (type == 1) E_val[0] = E_Grad + E_Data;
+		if (type == 2) E_val[0] = E_Grad;
+		return *E_val;
+}
diff --git a/src/Core/regularisers_CPU/utils.h b/src/Core/regularisers_CPU/utils.h
new file mode 100644
index 0000000..cfaf6d7
--- /dev/null
+++ b/src/Core/regularisers_CPU/utils.h
@@ -0,0 +1,34 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include <stdlib.h>
+#include <memory.h>
+#include "CCPiDefines.h"
+#include "omp.h"
+#ifdef __cplusplus
+extern "C" {
+#endif
+CCPI_EXPORT float copyIm(float *A, float *U, long dimX, long dimY, long dimZ);
+CCPI_EXPORT unsigned char copyIm_unchar(unsigned char *A, unsigned char *U, int dimX, int dimY, int dimZ);
+CCPI_EXPORT float copyIm_roll(float *A, float *U, int dimX, int dimY, int roll_value, int switcher);
+CCPI_EXPORT float TV_energy2D(float *U, float *U0, float *E_val, float lambda, int type, int dimX, int dimY);
+CCPI_EXPORT float TV_energy3D(float *U, float *U0, float *E_val, float lambda, int type, int dimX, int dimY, int dimZ);
+#ifdef __cplusplus
+}
+#endif
diff --git a/src/Core/regularisers_GPU/Diffus_4thO_GPU_core.cu b/src/Core/regularisers_GPU/Diffus_4thO_GPU_core.cu
new file mode 100644
index 0000000..a4dbe70
--- /dev/null
+++ b/src/Core/regularisers_GPU/Diffus_4thO_GPU_core.cu
@@ -0,0 +1,268 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "Diffus_4thO_GPU_core.h"
+#include "shared.h"
+
+/* CUDA implementation of fourth-order diffusion scheme [1] for piecewise-smooth recovery (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma)
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 5. tau - time-marching step for explicit scheme
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.
+ */
+
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+    
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+#define EPS 1.0e-7
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+__global__ void Weighted_Laplc2D_kernel(float *W_Lapl, float *U0, float sigma, int dimX, int dimY)
+{
+		int i1,i2,j1,j2;
+		float gradX, gradX_sq, gradY, gradY_sq, gradXX, gradYY, gradXY, xy_2, denom, V_norm, V_orth, c, c_sq;
+    
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            
+            /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+
+				gradX = 0.5f*(U0[j*dimX+i2] - U0[j*dimX+i1]);
+				gradX_sq = powf(gradX,2);
+				
+				gradY = 0.5f*(U0[j2*dimX+i] - U0[j1*dimX+i]);
+                gradY_sq = powf(gradY,2);
+                
+                gradXX = U0[j*dimX+i2] + U0[j*dimX+i1] - 2*U0[index];
+                gradYY = U0[j2*dimX+i] + U0[j1*dimX+i] - 2*U0[index];
+                
+                gradXY = 0.25f*(U0[j2*dimX+i2] + U0[j1*dimX+i1] - U0[j1*dimX+i2] - U0[j2*dimX+i1]);
+                xy_2 = 2.0f*gradX*gradY*gradXY;
+                
+                denom =  gradX_sq + gradY_sq;
+                
+                if (denom <= EPS) {
+                    V_norm = (gradXX*gradX_sq + xy_2 + gradYY*gradY_sq)/EPS;
+                    V_orth = (gradXX*gradY_sq - xy_2 + gradYY*gradX_sq)/EPS; 
+                    }
+                else  {
+                    V_norm = (gradXX*gradX_sq + xy_2 + gradYY*gradY_sq)/denom;
+                    V_orth = (gradXX*gradY_sq - xy_2 + gradYY*gradX_sq)/denom;  
+                    }
+
+                c = 1.0f/(1.0f + denom/sigma);
+                c_sq = c*c;
+                
+                W_Lapl[index] = c_sq*V_norm + c*V_orth;
+		}
+	return;
+} 
+
+__global__ void Diffusion_update_step2D_kernel(float *Output, float *Input, float *W_Lapl, float lambdaPar, float sigmaPar2, float tau, int dimX, int dimY)
+{
+	int i1,i2,j1,j2;
+    float gradXXc, gradYYc;
+
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            
+            /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+					
+                    gradXXc = W_Lapl[j*dimX+i2] + W_Lapl[j*dimX+i1] - 2*W_Lapl[index];
+                    gradYYc = W_Lapl[j2*dimX+i] + W_Lapl[j1*dimX+i] - 2*W_Lapl[index];
+
+                    Output[index] += tau*(-lambdaPar*(gradXXc + gradYYc) - (Output[index] - Input[index]));
+		}
+	return;
+} 
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+__global__ void Weighted_Laplc3D_kernel(float *W_Lapl, float *U0, float sigma, int dimX, int dimY, int dimZ)
+{
+		int i1,i2,j1,j2,k1,k2;
+		float gradX, gradX_sq, gradY, gradY_sq, gradXX, gradYY, gradXY, xy_2, denom, V_norm, V_orth, c, c_sq, gradZ, gradZ_sq, gradZZ, gradXZ, gradYZ, xyz_1, xyz_2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+		int j = blockDim.y * blockIdx.y + threadIdx.y;
+		int k = blockDim.z * blockIdx.z + threadIdx.z;
+		
+		if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+		    
+		    /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+			k1 = k+1; if (k1 == dimZ) k1 = k-1;
+			k2 = k-1; if (k2 < 0) k2 = k+1;
+		
+				int index = (dimX*dimY)*k + j*dimX+i;
+				
+				gradX = 0.5f*(U0[(dimX*dimY)*k + j*dimX+i2] - U0[(dimX*dimY)*k + j*dimX+i1]);
+				gradX_sq = pow(gradX,2);
+				
+				gradY = 0.5f*(U0[(dimX*dimY)*k + j2*dimX+i] - U0[(dimX*dimY)*k + j1*dimX+i]);
+                gradY_sq = pow(gradY,2);
+                
+                gradZ = 0.5f*(U0[(dimX*dimY)*k2 + j*dimX+i] - U0[(dimX*dimY)*k1 + j*dimX+i]);
+                gradZ_sq = pow(gradZ,2);
+                
+                gradXX = U0[(dimX*dimY)*k + j*dimX+i2] + U0[(dimX*dimY)*k + j*dimX+i1] - 2*U0[index];
+                gradYY = U0[(dimX*dimY)*k + j2*dimX+i] + U0[(dimX*dimY)*k + j1*dimX+i] - 2*U0[index];
+                gradZZ = U0[(dimX*dimY)*k2 + j*dimX+i] + U0[(dimX*dimY)*k1 + j*dimX+i] - 2*U0[index];
+                                
+                gradXY = 0.25f*(U0[(dimX*dimY)*k + j2*dimX+i2] + U0[(dimX*dimY)*k + j1*dimX+i1] - U0[(dimX*dimY)*k + j1*dimX+i2] - U0[(dimX*dimY)*k + j2*dimX+i1]);
+                gradXZ = 0.25f*(U0[(dimX*dimY)*k2 + j*dimX+i2] - U0[(dimX*dimY)*k2+j*dimX+i1] - U0[(dimX*dimY)*k1+j*dimX+i2] + U0[(dimX*dimY)*k1+j*dimX+i1]);
+                gradYZ = 0.25f*(U0[(dimX*dimY)*k2 +j2*dimX+i] - U0[(dimX*dimY)*k2+j1*dimX+i] - U0[(dimX*dimY)*k1+j2*dimX+i] + U0[(dimX*dimY)*k1+j1*dimX+i]);
+                
+                xy_2  = 2.0f*gradX*gradY*gradXY;
+                xyz_1 = 2.0f*gradX*gradZ*gradXZ;
+                xyz_2 = 2.0f*gradY*gradZ*gradYZ;
+                
+                denom =  gradX_sq + gradY_sq + gradZ_sq;
+                
+					if (denom <= EPS) {
+					V_norm = (gradXX*gradX_sq + gradYY*gradY_sq + gradZZ*gradZ_sq + xy_2 + xyz_1 + xyz_2)/EPS;
+                    V_orth = ((gradY_sq + gradZ_sq)*gradXX + (gradX_sq + gradZ_sq)*gradYY + (gradX_sq + gradY_sq)*gradZZ - xy_2 - xyz_1 - xyz_2)/EPS;
+					}
+					else  {
+					V_norm = (gradXX*gradX_sq + gradYY*gradY_sq + gradZZ*gradZ_sq + xy_2 + xyz_1 + xyz_2)/denom;
+                    V_orth = ((gradY_sq + gradZ_sq)*gradXX + (gradX_sq + gradZ_sq)*gradYY + (gradX_sq + gradY_sq)*gradZZ - xy_2 - xyz_1 - xyz_2)/denom;
+					}
+
+                c = 1.0f/(1.0f + denom/sigma);
+                c_sq = c*c;
+                
+            W_Lapl[index] = c_sq*V_norm + c*V_orth;
+		}
+	return;
+}
+__global__ void Diffusion_update_step3D_kernel(float *Output, float *Input, float *W_Lapl, float lambdaPar, float sigmaPar2, float tau, int dimX, int dimY, int dimZ)
+{
+	int i1,i2,j1,j2,k1,k2;
+    float gradXXc, gradYYc, gradZZc;
+
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+		int j = blockDim.y * blockIdx.y + threadIdx.y;
+		int k = blockDim.z * blockIdx.z + threadIdx.z;
+		
+		if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+		    
+		    /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == dimX) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == dimY) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+			k1 = k+1; if (k1 == dimZ) k1 = k-1;
+			k2 = k-1; if (k2 < 0) k2 = k+1;
+			
+			int index = (dimX*dimY)*k + j*dimX+i;
+			
+                    gradXXc = W_Lapl[(dimX*dimY)*k + j*dimX+i2] + W_Lapl[(dimX*dimY)*k + j*dimX+i1] - 2*W_Lapl[index];
+                    gradYYc = W_Lapl[(dimX*dimY)*k + j2*dimX+i] + W_Lapl[(dimX*dimY)*k + j1*dimX+i] - 2*W_Lapl[index];
+                    gradZZc = W_Lapl[(dimX*dimY)*k2 + j*dimX+i] + W_Lapl[(dimX*dimY)*k1 + j*dimX+i] - 2*W_Lapl[index];
+                    
+                    Output[index] += tau*(-lambdaPar*(gradXXc + gradYYc + gradZZc) - (Output[index] - Input[index]));
+		}
+	return;
+}
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+/********************* MAIN HOST FUNCTION ******************/
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+extern "C" int Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z)
+{
+		int dimTotal, dev = 0;
+		CHECK(cudaSetDevice(dev));
+        float *d_input, *d_output, *d_W_Lapl;
+        float sigmaPar2;
+        sigmaPar2 = sigmaPar*sigmaPar;
+        dimTotal = N*M*Z;
+        
+        CHECK(cudaMalloc((void**)&d_input,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_output,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_W_Lapl,dimTotal*sizeof(float)));
+                
+        CHECK(cudaMemcpy(d_input,Input,dimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(d_output,Input,dimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        
+	if (Z == 1) {
+	     /*2D case */
+        dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+        dim3 dimGrid(idivup(N,BLKXSIZE2D), idivup(M,BLKYSIZE2D));
+             
+        for(int n=0; n < iterationsNumb; n++) {
+				/* Calculating weighted Laplacian */
+				Weighted_Laplc2D_kernel<<<dimGrid,dimBlock>>>(d_W_Lapl, d_output, sigmaPar2, N, M);
+				CHECK(cudaDeviceSynchronize());
+				/* Perform iteration step */
+				Diffusion_update_step2D_kernel<<<dimGrid,dimBlock>>>(d_output, d_input, d_W_Lapl, lambdaPar, sigmaPar2, tau, N, M);
+				CHECK(cudaDeviceSynchronize());
+        }
+	}
+	else {
+		/*3D case*/
+        dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+        dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE),idivup(Z,BLKZSIZE));
+			for(int n=0; n < iterationsNumb; n++) {
+				/* Calculating weighted Laplacian */
+				Weighted_Laplc3D_kernel<<<dimGrid,dimBlock>>>(d_W_Lapl, d_output, sigmaPar2, N, M, Z);
+				CHECK(cudaDeviceSynchronize());
+				/* Perform iteration step */
+				Diffusion_update_step3D_kernel<<<dimGrid,dimBlock>>>(d_output, d_input, d_W_Lapl, lambdaPar, sigmaPar2, tau, N, M, Z);
+				CHECK(cudaDeviceSynchronize());
+			}
+		}
+        CHECK(cudaMemcpy(Output,d_output,dimTotal*sizeof(float),cudaMemcpyDeviceToHost));
+        CHECK(cudaFree(d_input));
+        CHECK(cudaFree(d_output));
+        CHECK(cudaFree(d_W_Lapl));
+        return 0;
+}
diff --git a/src/Core/regularisers_GPU/Diffus_4thO_GPU_core.h b/src/Core/regularisers_GPU/Diffus_4thO_GPU_core.h
new file mode 100644
index 0000000..77d5d79
--- /dev/null
+++ b/src/Core/regularisers_GPU/Diffus_4thO_GPU_core.h
@@ -0,0 +1,8 @@
+#ifndef __Diff_4thO_GPU_H__
+#define __Diff_4thO_GPU_H__
+#include "CCPiDefines.h"
+#include <stdio.h>
+
+extern "C" CCPI_EXPORT int Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/LLT_ROF_GPU_core.cu b/src/Core/regularisers_GPU/LLT_ROF_GPU_core.cu
new file mode 100644
index 0000000..87871be
--- /dev/null
+++ b/src/Core/regularisers_GPU/LLT_ROF_GPU_core.cu
@@ -0,0 +1,473 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "LLT_ROF_GPU_core.h"
+#include "shared.h"
+
+/* CUDA implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+ * 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+    
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+
+
+#define EPS_LLT 0.01
+#define EPS_ROF 1.0e-12
+
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+__host__ __device__ int signLLT (float x)
+{
+        return (x > 0) - (x < 0);
+}        
+   
+/*************************************************************************/
+/**********************LLT-related functions *****************************/
+/*************************************************************************/
+__global__ void der2D_LLT_kernel(float *U, float *D1, float *D2, int dimX, int dimY)
+    {
+		int i_p, i_m, j_m, j_p;
+		float dxx, dyy, denom_xx, denom_yy;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+
+			dxx = U[j*dimX+i_p] - 2.0f*U[index] + U[j*dimX+i_m];
+			dyy = U[j_p*dimX+i] - 2.0f*U[index] + U[j_m*dimX+i];
+
+			denom_xx = abs(dxx) + EPS_LLT;
+			denom_yy = abs(dyy) + EPS_LLT;
+
+			D1[index] = dxx / denom_xx;
+			D2[index] = dyy / denom_yy;
+		}
+	}
+	
+__global__ void der3D_LLT_kernel(float* U, float *D1, float *D2, float *D3, int dimX, int dimY, int dimZ)
+    {
+		int i_p, i_m, j_m, j_p, k_p, k_m;
+		float dxx, dyy, dzz, denom_xx, denom_yy, denom_zz;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+			
+        /* symmetric boundary conditions (Neuman) */
+ 		i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+ 		i_m = i - 1; if (i_m < 0) i_m = i + 1;
+ 		j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+ 		j_m = j - 1; if (j_m < 0) j_m = j + 1;
+ 		k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+ 		k_m = k - 1; if (k_m < 0) k_m = k + 1;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;
+      	
+      	dxx = U[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*U[index] + U[(dimX*dimY)*k + j*dimX+i_m];
+ 		dyy = U[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k + j_m*dimX+i];
+ 		dzz = U[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*U[index] + U[(dimX*dimY)*k_m + j*dimX+i];
+ 
+ 		denom_xx = abs(dxx) + EPS_LLT;
+ 		denom_yy = abs(dyy) + EPS_LLT;
+ 		denom_zz = abs(dzz) + EPS_LLT;
+ 
+ 		D1[index] = dxx / denom_xx;
+ 		D2[index] = dyy / denom_yy;
+ 		D3[index] = dzz / denom_zz;
+		}
+	}
+
+/*************************************************************************/
+/**********************ROF-related functions *****************************/
+/*************************************************************************/
+
+/* first-order differences 1 */
+__global__ void D1_func2D_ROF_kernel(float* Input, float* D1, int N, int M)
+    {
+		int i1, j1, i2;
+		float NOMx_1,NOMy_1,NOMy_0,denom1,denom2,T1;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;        
+        
+        if ((i >= 0) && (i < N) && (j >= 0) && (j < M)) {
+            
+            /* boundary conditions (Neumann reflections) */
+                i1 = i + 1; if (i1 >= N) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= M) j1 = j-1;
+		
+		     /* Forward-backward differences */
+                NOMx_1 = Input[j1*N + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[j*N + i1] - Input[index]; /* y+ */                
+                NOMy_0 = Input[index] - Input[j*N + i2]; /* y- */
+                
+                denom1 = NOMx_1*NOMx_1;
+                denom2 = 0.5f*(signLLT((float)NOMy_1) + signLLT((float)NOMy_0))*(MIN(abs((float)NOMy_1),abs((float)NOMy_0)));
+                denom2 = denom2*denom2;
+                T1 = sqrt(denom1 + denom2 + EPS_ROF);
+                D1[index] = NOMx_1/T1;
+		}		
+	}
+	
+/* differences 2 */
+__global__ void D2_func2D_ROF_kernel(float* Input, float* D2, int N, int M)      
+    {
+		int i1, j1, j2;
+		float NOMx_1,NOMy_1,NOMx_0,denom1,denom2,T2;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;        
+        
+        if ((i >= 0) && (i < (N)) && (j >= 0) && (j < (M))) {
+            
+            /* boundary conditions (Neumann reflections) */
+                i1 = i + 1; if (i1 >= N) i1 = i-1;
+                j1 = j + 1; if (j1 >= M) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1; 
+		
+                /* Forward-backward differences */
+                NOMx_1 = Input[j1*N + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[j*N + i1] - Input[index]; /* y+ */
+                NOMx_0 = Input[index] - Input[j2*N + i]; /* x- */
+                
+                denom1 = NOMy_1*NOMy_1;
+                denom2 = 0.5f*(signLLT((float)NOMx_1) + signLLT((float)NOMx_0))*(MIN(abs((float)NOMx_1),abs((float)NOMx_0)));
+                denom2 = denom2*denom2;
+                T2 = sqrt(denom1 + denom2 + EPS_ROF);
+                D2[index] = NOMy_1/T2;	
+		}		
+	}
+
+ 
+    /* differences 1 */
+__global__ void D1_func3D_ROF_kernel(float* Input, float* D1, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMy_0, NOMz_1, NOMz_0, denom1, denom2,denom3, T1;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+            
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = Input[(dimX*dimY)*k + j1*dimX + i] - Input[index]; /* x+ */
+                    NOMy_1 = Input[(dimX*dimY)*k + j*dimX + i1] - Input[index]; /* y+ */                    
+                    NOMy_0 = Input[index] - Input[(dimX*dimY)*k + j*dimX + i2]; /* y- */
+                    
+                    NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+                    NOMz_0 = Input[index] - Input[(dimX*dimY)*k2 + j*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMx_1*NOMx_1;
+                    denom2 = 0.5*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(abs(NOMy_1),abs(NOMy_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(abs(NOMz_1),abs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T1 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                    D1[index] = NOMx_1/T1;	
+		}		
+	}      
+
+    /* differences 2 */
+    __global__ void D2_func3D_ROF_kernel(float* Input, float* D2, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;     
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = Input[(dimX*dimY)*k + (j1)*dimX + i] - Input[index]; /* x+ */
+                    NOMy_1 = Input[(dimX*dimY)*k + (j)*dimX + i1] - Input[index]; /* y+ */
+                    NOMx_0 = Input[index] - Input[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                    NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+                    NOMz_0 = Input[index] - Input[(dimX*dimY)*k2 + (j)*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMy_1*NOMy_1;
+                    denom2 = 0.5*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(abs(NOMx_1),abs(NOMx_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5*(signLLT(NOMz_1) + signLLT(NOMz_0))*(MIN(abs(NOMz_1),abs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T2 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                    D2[index] = NOMy_1/T2;
+		}
+	}
+	
+	  /* differences 3 */
+    __global__ void D3_func3D_ROF_kernel(float* Input, float* D3, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMx_0, NOMy_0, NOMz_1, denom1, denom2, denom3, T3;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;     
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+
+				i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                k2 = k - 1; if (k2 < 0) k2 = k+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = Input[(dimX*dimY)*k + (j1)*dimX + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[(dimX*dimY)*k + (j)*dimX + i1] - Input[index]; /* y+ */
+                NOMy_0 = Input[index] - Input[(dimX*dimY)*k + (j)*dimX + i2]; /* y- */
+                NOMx_0 = Input[index] - Input[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+               
+                denom1 = NOMz_1*NOMz_1;
+                denom2 = 0.5*(signLLT(NOMx_1) + signLLT(NOMx_0))*(MIN(abs(NOMx_1),abs(NOMx_0)));
+                denom2 = denom2*denom2;
+                denom3 = 0.5*(signLLT(NOMy_1) + signLLT(NOMy_0))*(MIN(abs(NOMy_1),abs(NOMy_0)));
+                denom3 = denom3*denom3;
+                T3 = sqrt(denom1 + denom2 + denom3 + EPS_ROF);
+                D3[index] = NOMz_1/T3;
+		}
+	}
+/*************************************************************************/
+/**********************ROF-LLT-related functions *************************/
+/*************************************************************************/
+
+__global__ void Update2D_LLT_ROF_kernel(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D1_ROF, float *D2_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY)
+{
+		
+		int i_p, i_m, j_m, j_p;
+		float div, laplc, dxx, dyy, dv1, dv2;
+	
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+
+			index = j*dimX+i;
+					
+			/*LLT-related part*/
+			dxx = D1_LLT[j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[j*dimX+i_m];
+			dyy = D2_LLT[j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[j_m*dimX+i];
+			laplc = dxx + dyy; /*build Laplacian*/
+			/*ROF-related part*/
+			dv1 = D1_ROF[index] - D1_ROF[j_m*dimX + i];
+            dv2 = D2_ROF[index] - D2_ROF[j*dimX + i_m];
+			div = dv1 + dv2; /*build Divirgent*/
+            
+			/*combine all into one cost function to minimise */
+            U[index] += tau*(2.0f*lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+		}
+}
+
+__global__ void Update3D_LLT_ROF_kernel(float *U0, float *U, float *D1_LLT, float *D2_LLT, float *D3_LLT, float *D1_ROF, float *D2_ROF, float *D3_ROF, float lambdaROF, float lambdaLLT, float tau, int dimX, int dimY, int dimZ)
+{
+	int i_p, i_m, j_m, j_p, k_p, k_m;
+	float div, laplc, dxx, dyy, dzz, dv1, dv2, dv3;
+	
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+			
+			/* symmetric boundary conditions (Neuman) */
+			i_p = i + 1; if (i_p == dimX) i_p = i - 1;
+			i_m = i - 1; if (i_m < 0) i_m = i + 1;
+			j_p = j + 1; if (j_p == dimY) j_p = j - 1;
+			j_m = j - 1; if (j_m < 0) j_m = j + 1;
+			k_p = k + 1; if (k_p == dimZ) k_p = k - 1;
+			k_m = k - 1; if (k_m < 0) k_m = k + 1;
+        
+			int index = (dimX*dimY)*k + j*dimX+i;
+      	
+			/*LLT-related part*/
+			dxx = D1_LLT[(dimX*dimY)*k + j*dimX+i_p] - 2.0f*D1_LLT[index] + D1_LLT[(dimX*dimY)*k + j*dimX+i_m];
+			dyy = D2_LLT[(dimX*dimY)*k + j_p*dimX+i] - 2.0f*D2_LLT[index] + D2_LLT[(dimX*dimY)*k + j_m*dimX+i];
+			dzz = D3_LLT[(dimX*dimY)*k_p + j*dimX+i] - 2.0f*D3_LLT[index] + D3_LLT[(dimX*dimY)*k_m + j*dimX+i];
+			laplc = dxx + dyy + dzz; /*build Laplacian*/
+			
+			/*ROF-related part*/
+			dv1 = D1_ROF[index] - D1_ROF[(dimX*dimY)*k + j_m*dimX+i];
+            dv2 = D2_ROF[index] - D2_ROF[(dimX*dimY)*k + j*dimX+i_m];
+            dv3 = D3_ROF[index] - D3_ROF[(dimX*dimY)*k_m + j*dimX+i];
+			div = dv1 + dv2 + dv3; /*build Divirgent*/
+            
+			/*combine all into one cost function to minimise */
+            U[index] += tau*(2.0f*lambdaROF*(div) - lambdaLLT*(laplc) - (U[index] - U0[index]));
+        }
+}
+
+/*******************************************************************/
+/************************ HOST FUNCTION ****************************/
+/*******************************************************************/
+
+extern "C" int LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z)
+{
+	    // set up device
+		int dev = 0;
+		int DimTotal;
+		DimTotal = N*M*Z;
+		CHECK(cudaSetDevice(dev));
+        float *d_input, *d_update;
+        float *D1_LLT=NULL, *D2_LLT=NULL, *D3_LLT=NULL, *D1_ROF=NULL, *D2_ROF=NULL, *D3_ROF=NULL;
+        
+	if (Z == 0) {Z = 1;}
+	
+        CHECK(cudaMalloc((void**)&d_input,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_update,DimTotal*sizeof(float)));
+        
+        CHECK(cudaMalloc((void**)&D1_LLT,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D2_LLT,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D3_LLT,DimTotal*sizeof(float)));
+        
+        CHECK(cudaMalloc((void**)&D1_ROF,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D2_ROF,DimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&D3_ROF,DimTotal*sizeof(float)));
+        
+        CHECK(cudaMemcpy(d_input,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(d_update,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        
+    if (Z == 1) {
+			// TV - 2D case
+            dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+            dim3 dimGrid(idivup(N,BLKXSIZE2D), idivup(M,BLKYSIZE2D));
+             
+            for(int n=0; n < iterationsNumb; n++) {
+                /****************ROF******************/
+				/* calculate first-order differences */
+                D1_func2D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D1_ROF, N, M);
+                CHECK(cudaDeviceSynchronize());
+				D2_func2D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D2_ROF, N, M);
+                CHECK(cudaDeviceSynchronize());                
+                /****************LLT******************/
+                 /* estimate second-order derrivatives */
+				der2D_LLT_kernel<<<dimGrid,dimBlock>>>(d_update, D1_LLT, D2_LLT, N, M);
+				/* Joint update for ROF and LLT models */
+				Update2D_LLT_ROF_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, D1_LLT, D2_LLT, D1_ROF, D2_ROF, lambdaROF, lambdaLLT, tau, N, M);
+                CHECK(cudaDeviceSynchronize());
+            }
+    }
+    else {
+			// 3D case
+            dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+            dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE),idivup(Z,BLKXSIZE));
+           
+            for(int n=0; n < iterationsNumb; n++) {
+                /****************ROF******************/
+				/* calculate first-order differences */
+                D1_func3D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D1_ROF, N, M, Z);
+                CHECK(cudaDeviceSynchronize());
+				D2_func3D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D2_ROF, N, M, Z);
+                CHECK(cudaDeviceSynchronize());        
+                D3_func3D_ROF_kernel<<<dimGrid,dimBlock>>>(d_update, D3_ROF, N, M, Z);
+                CHECK(cudaDeviceSynchronize());        
+                /****************LLT******************/
+                 /* estimate second-order derrivatives */
+				der3D_LLT_kernel<<<dimGrid,dimBlock>>>(d_update, D1_LLT, D2_LLT, D3_LLT, N, M, Z);
+				/* Joint update for ROF and LLT models */
+				Update3D_LLT_ROF_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, D1_LLT, D2_LLT, D3_LLT, D1_ROF, D2_ROF, D3_ROF, lambdaROF, lambdaLLT, tau, N, M, Z);
+                CHECK(cudaDeviceSynchronize());
+            }
+    }        
+        CHECK(cudaMemcpy(Output,d_update,DimTotal*sizeof(float),cudaMemcpyDeviceToHost));
+        CHECK(cudaFree(d_input));
+        CHECK(cudaFree(d_update));
+        CHECK(cudaFree(D1_LLT));
+        CHECK(cudaFree(D2_LLT));
+        CHECK(cudaFree(D3_LLT));
+        CHECK(cudaFree(D1_ROF));
+        CHECK(cudaFree(D2_ROF));
+        CHECK(cudaFree(D3_ROF));
+        return 0;
+}
diff --git a/src/Core/regularisers_GPU/LLT_ROF_GPU_core.h b/src/Core/regularisers_GPU/LLT_ROF_GPU_core.h
new file mode 100644
index 0000000..a6bfcc7
--- /dev/null
+++ b/src/Core/regularisers_GPU/LLT_ROF_GPU_core.h
@@ -0,0 +1,8 @@
+#ifndef __ROFLLTGPU_H__
+#define __ROFLLTGPU_H__
+#include "CCPiDefines.h"
+#include <stdio.h>
+
+extern "C" CCPI_EXPORT int LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/NonlDiff_GPU_core.cu b/src/Core/regularisers_GPU/NonlDiff_GPU_core.cu
new file mode 100644
index 0000000..ff7ce4d
--- /dev/null
+++ b/src/Core/regularisers_GPU/NonlDiff_GPU_core.cu
@@ -0,0 +1,345 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "NonlDiff_GPU_core.h"
+#include "shared.h"
+
+/* CUDA implementation of linear and nonlinear diffusion with the regularisation model [1,2] (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 5. tau - time-marching step for explicit scheme
+ * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+ */
+
+
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+    
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+#define EPS 1.0e-5
+    
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+__host__ __device__ int signNDF (float x)
+{
+        return (x > 0) - (x < 0);
+}        
+   
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+__global__ void LinearDiff2D_kernel(float *Input, float *Output, float lambdaPar, float tau, int N, int M)
+    {
+		int i1,i2,j1,j2;
+		float e,w,n,s,e1,w1,n1,s1;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;
+        
+        if ((i >= 0) && (i < N) && (j >= 0) && (j < M)) {
+            
+            /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == N) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == M) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            
+		        e = Output[j*N+i1];
+                w = Output[j*N+i2];
+                n = Output[j1*N+i];
+                s = Output[j2*N+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index])); 
+		}
+	} 
+    
+ __global__ void NonLinearDiff2D_kernel(float *Input, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, int N, int M)
+    {
+		int i1,i2,j1,j2;
+		float e,w,n,s,e1,w1,n1,s1;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;
+        
+        if ((i >= 0) && (i < N) && (j >= 0) && (j < M)) {
+            
+            /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == N) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == M) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+            
+		        e = Output[j*N+i1];
+                w = Output[j*N+i2];
+                n = Output[j1*N+i];
+                s = Output[j2*N+i];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                
+            if (penaltytype == 1){
+            /* Huber penalty */
+            if (abs(e1) > sigmaPar) e1 =  signNDF(e1);
+            else e1 = e1/sigmaPar;
+            
+            if (abs(w1) > sigmaPar) w1 =  signNDF(w1);
+            else w1 = w1/sigmaPar;
+            
+            if (abs(n1) > sigmaPar) n1 =  signNDF(n1);
+            else n1 = n1/sigmaPar;
+            
+            if (abs(s1) > sigmaPar) s1 =  signNDF(s1);
+            else s1 = s1/sigmaPar;
+            }
+            else if (penaltytype == 2) {
+            /* Perona-Malik */
+            e1 = (e1)/(1.0f + pow((e1/sigmaPar),2));
+            w1 = (w1)/(1.0f + pow((w1/sigmaPar),2));
+            n1 = (n1)/(1.0f + pow((n1/sigmaPar),2));
+            s1 = (s1)/(1.0f + pow((s1/sigmaPar),2));
+            }
+            else if (penaltytype == 3) {
+            /* Tukey Biweight */
+            if (abs(e1) <= sigmaPar) e1 =  e1*pow((1.0f - pow((e1/sigmaPar),2)), 2);
+            else e1 = 0.0f;
+            if (abs(w1) <= sigmaPar) w1 =  w1*pow((1.0f - pow((w1/sigmaPar),2)), 2);
+            else w1 = 0.0f;
+            if (abs(n1) <= sigmaPar) n1 =  n1*pow((1.0f - pow((n1/sigmaPar),2)), 2);
+            else n1 = 0.0f;
+            if (abs(s1) <= sigmaPar) s1 =  s1*pow((1.0f - pow((s1/sigmaPar),2)), 2);
+            else s1 = 0.0f;
+            }
+            else printf("%s \n", "No penalty function selected! Use 1,2 or 3.");
+                            
+            Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1) - (Output[index] - Input[index])); 
+		}
+	} 
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+
+__global__ void LinearDiff3D_kernel(float *Input, float *Output, float lambdaPar, float tau, int N, int M, int Z)
+    {
+		int i1,i2,j1,j2,k1,k2;
+		float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+		int j = blockDim.y * blockIdx.y + threadIdx.y;
+		int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+		int index = (N*M)*k + i + N*j;        
+        
+        if ((i >= 0) && (i < N) && (j >= 0) && (j < M) && (k >= 0) && (k < Z)) {
+            
+            /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == N) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == M) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+			k1 = k+1; if (k1 == Z) k1 = k-1;
+			k2 = k-1; if (k2 < 0) k2 = k+1;
+            
+		        e = Output[(N*M)*k + i1 + N*j];
+                w = Output[(N*M)*k + i2 + N*j];
+                n = Output[(N*M)*k + i + N*j1];
+                s = Output[(N*M)*k + i + N*j2];
+                u = Output[(N*M)*k1 + i + N*j];
+                d = Output[(N*M)*k2 + i + N*j];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                u1 = u - Output[index];
+                d1 = d - Output[index];
+                
+                Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index])); 
+		}
+	} 
+
+__global__ void NonLinearDiff3D_kernel(float *Input, float *Output, float lambdaPar, float sigmaPar, float tau, int penaltytype, int N, int M, int Z)
+    {
+		int i1,i2,j1,j2,k1,k2;
+		float e,w,n,s,u,d,e1,w1,n1,s1,u1,d1;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+		int j = blockDim.y * blockIdx.y + threadIdx.y;
+		int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+		int index = (N*M)*k + i + N*j;        
+        
+        if ((i >= 0) && (i < N) && (j >= 0) && (j < M) && (k >= 0) && (k < Z)) {
+            
+            /* boundary conditions (Neumann reflections) */
+			i1 = i+1; if (i1 == N) i1 = i-1;
+			i2 = i-1; if (i2 < 0) i2 = i+1;
+            j1 = j+1; if (j1 == M) j1 = j-1;
+            j2 = j-1; if (j2 < 0) j2 = j+1;
+			k1 = k+1; if (k1 == Z) k1 = k-1;
+			k2 = k-1; if (k2 < 0) k2 = k+1;
+            
+		        e = Output[(N*M)*k + i1 + N*j];
+                w = Output[(N*M)*k + i2 + N*j];
+                n = Output[(N*M)*k + i + N*j1];
+                s = Output[(N*M)*k + i + N*j2];
+                u = Output[(N*M)*k1 + i + N*j];
+                d = Output[(N*M)*k2 + i + N*j];
+                
+                e1 = e - Output[index];
+                w1 = w - Output[index];
+                n1 = n - Output[index];
+                s1 = s - Output[index];
+                u1 = u - Output[index];
+                d1 = d - Output[index];
+                
+                
+            if (penaltytype == 1){
+            /* Huber penalty */
+            if (abs(e1) > sigmaPar) e1 =  signNDF(e1);
+            else e1 = e1/sigmaPar;
+            
+            if (abs(w1) > sigmaPar) w1 =  signNDF(w1);
+            else w1 = w1/sigmaPar;
+            
+            if (abs(n1) > sigmaPar) n1 =  signNDF(n1);
+            else n1 = n1/sigmaPar;
+            
+            if (abs(s1) > sigmaPar) s1 =  signNDF(s1);
+            else s1 = s1/sigmaPar;
+            
+            if (abs(u1) > sigmaPar) u1 =  signNDF(u1);
+            else u1 = u1/sigmaPar;
+            
+            if (abs(d1) > sigmaPar) d1 =  signNDF(d1);
+            else d1 = d1/sigmaPar;            
+            }
+            else if (penaltytype == 2) {
+            /* Perona-Malik */
+            e1 = (e1)/(1.0f + pow((e1/sigmaPar),2));
+            w1 = (w1)/(1.0f + pow((w1/sigmaPar),2));
+            n1 = (n1)/(1.0f + pow((n1/sigmaPar),2));
+            s1 = (s1)/(1.0f + pow((s1/sigmaPar),2));
+            u1 = (u1)/(1.0f + pow((u1/sigmaPar),2));
+            d1 = (d1)/(1.0f + pow((d1/sigmaPar),2));
+            }
+            else if (penaltytype == 3) {
+            /* Tukey Biweight */
+            if (abs(e1) <= sigmaPar) e1 =  e1*pow((1.0f - pow((e1/sigmaPar),2)), 2);
+            else e1 = 0.0f;
+            if (abs(w1) <= sigmaPar) w1 =  w1*pow((1.0f - pow((w1/sigmaPar),2)), 2);
+            else w1 = 0.0f;
+            if (abs(n1) <= sigmaPar) n1 =  n1*pow((1.0f - pow((n1/sigmaPar),2)), 2);
+            else n1 = 0.0f;
+            if (abs(s1) <= sigmaPar) s1 =  s1*pow((1.0f - pow((s1/sigmaPar),2)), 2);
+            else s1 = 0.0f;
+            if (abs(u1) <= sigmaPar) u1 =  u1*pow((1.0f - pow((u1/sigmaPar),2)), 2);
+            else u1 = 0.0f;
+            if (abs(d1) <= sigmaPar) d1 =  d1*pow((1.0f - pow((d1/sigmaPar),2)), 2);
+            else d1 = 0.0f;
+            }
+            else printf("%s \n", "No penalty function selected! Use 1,2 or 3.");
+
+            Output[index] += tau*(lambdaPar*(e1 + w1 + n1 + s1 + u1 + d1) - (Output[index] - Input[index])); 
+		}
+	} 
+
+/////////////////////////////////////////////////
+// HOST FUNCTION
+extern "C" int NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z)
+{
+	    // set up device
+		int dev = 0;
+		CHECK(cudaSetDevice(dev));
+        float *d_input, *d_output;
+        float sigmaPar2;
+        sigmaPar2 = sigmaPar/sqrt(2.0f);
+        
+        CHECK(cudaMalloc((void**)&d_input,N*M*Z*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_output,N*M*Z*sizeof(float)));
+                
+        CHECK(cudaMemcpy(d_input,Input,N*M*Z*sizeof(float),cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(d_output,Input,N*M*Z*sizeof(float),cudaMemcpyHostToDevice));      
+        
+	if (Z == 1) {
+	     /*2D case */ 
+        
+        dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+        dim3 dimGrid(idivup(N,BLKXSIZE2D), idivup(M,BLKYSIZE2D));
+             
+        for(int n=0; n < iterationsNumb; n++) {
+				if (sigmaPar == 0.0f) {
+				/* linear diffusion (heat equation) */
+				LinearDiff2D_kernel<<<dimGrid,dimBlock>>>(d_input, d_output, lambdaPar, tau, N, M);
+				CHECK(cudaDeviceSynchronize());
+				}
+				else {
+				/* nonlinear diffusion */
+				NonLinearDiff2D_kernel<<<dimGrid,dimBlock>>>(d_input, d_output, lambdaPar, sigmaPar2, tau, penaltytype, N, M);
+				CHECK(cudaDeviceSynchronize());
+				}
+        }
+	}
+	else {
+		/*3D case*/
+        dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+        dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE),idivup(Z,BLKZSIZE));
+			for(int n=0; n < iterationsNumb; n++) {
+				if (sigmaPar == 0.0f) {
+				/* linear diffusion (heat equation) */
+				LinearDiff3D_kernel<<<dimGrid,dimBlock>>>(d_input, d_output, lambdaPar, tau, N, M, Z);
+				CHECK(cudaDeviceSynchronize());
+				}
+				else {
+				/* nonlinear diffusion */
+				NonLinearDiff3D_kernel<<<dimGrid,dimBlock>>>(d_input, d_output, lambdaPar, sigmaPar2, tau, penaltytype, N, M, Z);
+				CHECK(cudaDeviceSynchronize());
+				}
+			}
+        
+		}        
+        CHECK(cudaMemcpy(Output,d_output,N*M*Z*sizeof(float),cudaMemcpyDeviceToHost));
+        CHECK(cudaFree(d_input));
+        CHECK(cudaFree(d_output));
+        //cudaDeviceReset();
+        return 0;
+}
diff --git a/src/Core/regularisers_GPU/NonlDiff_GPU_core.h b/src/Core/regularisers_GPU/NonlDiff_GPU_core.h
new file mode 100644
index 0000000..5fe457e
--- /dev/null
+++ b/src/Core/regularisers_GPU/NonlDiff_GPU_core.h
@@ -0,0 +1,8 @@
+#ifndef __NonlDiffGPU_H__
+#define __NonlDiffGPU_H__
+#include "CCPiDefines.h"
+#include <stdio.h>
+
+extern "C" CCPI_EXPORT int NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/PatchSelect_GPU_core.cu b/src/Core/regularisers_GPU/PatchSelect_GPU_core.cu
new file mode 100644
index 0000000..98c8488
--- /dev/null
+++ b/src/Core/regularisers_GPU/PatchSelect_GPU_core.cu
@@ -0,0 +1,460 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC and Diamond Light Source Ltd. 
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ * Copyright 2018 Diamond Light Source Ltd. 
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "PatchSelect_GPU_core.h"
+#include "shared.h"
+
+/* CUDA implementation of non-local weight pre-calculation for non-local priors
+ * Weights and associated indices are stored into pre-allocated arrays and passed
+ * to the regulariser
+ *
+ *
+ * Input Parameters:
+ * 1. 2D grayscale image (classical 3D version will not be supported but rather 2D + dim extension (TODO))
+ * 2. Searching window (half-size of the main bigger searching window, e.g. 11)
+ * 3. Similarity window (half-size of the patch window, e.g. 2)
+ * 4. The number of neighbours to take (the most prominent after sorting neighbours will be taken)
+ * 5. noise-related parameter to calculate non-local weights
+ *
+ * Output [2D]:
+ * 1. AR_i - indeces of i neighbours
+ * 2. AR_j - indeces of j neighbours
+ * 3. Weights_ij - associated weights
+ */
+
+
+#define BLKXSIZE 16
+#define BLKYSIZE 16
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+#define M_PI 3.14159265358979323846
+#define EPS 1.0e-8
+#define CONSTVECSIZE5 121
+#define CONSTVECSIZE7 225
+#define CONSTVECSIZE9 361
+#define CONSTVECSIZE11 529
+#define CONSTVECSIZE13 729
+
+__device__ void swap(float *xp, float *yp) 
+{
+    float temp = *xp; 
+    *xp = *yp; 
+    *yp = temp; 
+}
+__device__ void swapUS(unsigned short *xp, unsigned short *yp) 
+{ 
+    unsigned short temp = *xp; 
+    *xp = *yp; 
+    *yp = temp; 
+}
+
+/********************************************************************************/
+__global__ void IndexSelect2D_5_kernel(float *Ad, unsigned short *H_i_d, unsigned short *H_j_d, float *Weights_d, float *Eucl_Vec_d, int N, int M, int SearchWindow, int SearchW_full, int SimilarWin, int NumNeighb, float h2)
+{          
+
+    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, counterG, index2;
+    float normsum;
+    
+    float Weight_Vec[CONSTVECSIZE5];
+    unsigned short ind_i[CONSTVECSIZE5];
+    unsigned short ind_j[CONSTVECSIZE5];
+
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    long index = i*M+j;      
+    
+    counter = 0;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            i1 = i+i_m;
+            j1 = j+j_m;
+              if (((i1 >= 0) && (i1 < N)) && ((j1 >= 0) && (j1 < M))) {
+                normsum = 0.0f; counterG = 0;
+                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                        i2 = i1 + i_c;
+                        j2 = j1 + j_c;
+                        i3 = i + i_c;
+                        j3 = j + j_c;
+                        if (((i2 >= 0) && (i2 < N)) && ((j2 >= 0) && (j2 < M))) {
+                            if (((i3 >= 0) && (i3 < N)) && ((j3 >= 0) && (j3 < M))) {
+                                normsum += Eucl_Vec_d[counterG]*powf(Ad[i3*M + j3] - Ad[i2*M + j2], 2);                                
+                                counterG++;
+                            }}                        
+                     }}
+                /* writing temporarily into vectors */
+                if (normsum > EPS) {
+                    Weight_Vec[counter] = __expf(-normsum/h2);
+                    ind_i[counter] = i1;
+                    ind_j[counter] = j1;
+                    counter++;
+                }
+             }
+        }}
+        
+    /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter-1; x++)  {
+       for (y = 0; y < counter-x-1; y++)  {
+           if (Weight_Vec[y] < Weight_Vec[y+1]) {
+            swap(&Weight_Vec[y], &Weight_Vec[y+1]); 		                       
+            swapUS(&ind_i[y], &ind_i[y+1]);
+            swapUS(&ind_j[y], &ind_j[y+1]);  
+            }
+    	}
+    }     
+    /*sorting loop finished*/        
+    /*now select the NumNeighb more prominent weights and store into arrays */     
+    for(x=0; x < NumNeighb; x++) {
+        index2 = (N*M*x) + index;
+        H_i_d[index2] = ind_i[x];
+        H_j_d[index2] = ind_j[x];
+        Weights_d[index2] = Weight_Vec[x];
+    }
+} 
+/********************************************************************************/
+__global__ void IndexSelect2D_7_kernel(float *Ad, unsigned short *H_i_d, unsigned short *H_j_d, float *Weights_d, float *Eucl_Vec_d, int N, int M, int SearchWindow, int SearchW_full, int SimilarWin, int NumNeighb, float h2)
+{          
+
+    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, counterG, index2;
+    float normsum;
+    
+    float Weight_Vec[CONSTVECSIZE7];
+    unsigned short ind_i[CONSTVECSIZE7];
+    unsigned short ind_j[CONSTVECSIZE7];
+
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    long index = i*M+j;      
+    
+    counter = 0;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            i1 = i+i_m;
+            j1 = j+j_m;
+             if (((i1 >= 0) && (i1 < N)) && ((j1 >= 0) && (j1 < M))) {
+                normsum = 0.0f; counterG = 0;
+                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                        i2 = i1 + i_c;
+                        j2 = j1 + j_c;
+                        i3 = i + i_c;
+                        j3 = j + j_c;
+                        if (((i2 >= 0) && (i2 < N)) && ((j2 >= 0) && (j2 < M))) {
+                            if (((i3 >= 0) && (i3 < N)) && ((j3 >= 0) && (j3 < M))) {
+                                normsum += Eucl_Vec_d[counterG]*powf(Ad[i3*M + j3] - Ad[i2*M + j2], 2);                                
+                                counterG++;
+                            }}                        
+                     }}
+                /* writing temporarily into vectors */
+                if (normsum > EPS) {
+                    Weight_Vec[counter] = __expf(-normsum/h2);
+                    ind_i[counter] = i1;
+                    ind_j[counter] = j1;
+                    counter++;
+                }
+             }
+        }}
+        
+    /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter-1; x++)  {
+       for (y = 0; y < counter-x-1; y++)  {
+           if (Weight_Vec[y] < Weight_Vec[y+1]) {
+            swap(&Weight_Vec[y], &Weight_Vec[y+1]); 		                       
+            swapUS(&ind_i[y], &ind_i[y+1]);
+            swapUS(&ind_j[y], &ind_j[y+1]);  
+            }
+    	}
+    }     
+    /*sorting loop finished*/        
+    /*now select the NumNeighb more prominent weights and store into arrays */     
+    for(x=0; x < NumNeighb; x++) {
+        index2 = (N*M*x) + index;
+        H_i_d[index2] = ind_i[x];
+        H_j_d[index2] = ind_j[x];
+        Weights_d[index2] = Weight_Vec[x];
+    }
+}
+__global__ void IndexSelect2D_9_kernel(float *Ad, unsigned short *H_i_d, unsigned short *H_j_d, float *Weights_d, float *Eucl_Vec_d, int N, int M, int SearchWindow, int SearchW_full, int SimilarWin, int NumNeighb, float h2)
+{          
+
+    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, counterG, index2;
+    float normsum;
+  
+    float Weight_Vec[CONSTVECSIZE9];
+    unsigned short ind_i[CONSTVECSIZE9];
+    unsigned short ind_j[CONSTVECSIZE9];
+
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    long index = i*M+j;      
+    
+    counter = 0;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            i1 = i+i_m;
+            j1 = j+j_m;
+            if (((i1 >= 0) && (i1 < N)) && ((j1 >= 0) && (j1 < M))) {
+                normsum = 0.0f; counterG = 0;
+                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                        i2 = i1 + i_c;
+                        j2 = j1 + j_c;
+                        i3 = i + i_c;
+                        j3 = j + j_c;
+                        if (((i2 >= 0) && (i2 < N)) && ((j2 >= 0) && (j2 < M))) {
+                            if (((i3 >= 0) && (i3 < N)) && ((j3 >= 0) && (j3 < M))) {
+                                normsum += Eucl_Vec_d[counterG]*powf(Ad[i3*M + j3] - Ad[i2*M + j2], 2);
+                                counterG++;
+                            }}                        
+                     }}
+                /* writing temporarily into vectors */
+                if (normsum > EPS) {
+                    Weight_Vec[counter] = expf(-normsum/h2);
+                    ind_i[counter] = i1;
+                    ind_j[counter] = j1;
+                    counter++;
+                }
+            }
+        }}
+        
+    /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter-1; x++)  {
+       for (y = 0; y < counter-x-1; y++)  {
+           if (Weight_Vec[y] < Weight_Vec[y+1]) {
+            swap(&Weight_Vec[y], &Weight_Vec[y+1]); 		                       
+            swapUS(&ind_i[y], &ind_i[y+1]);
+            swapUS(&ind_j[y], &ind_j[y+1]);  
+            }
+    	}
+    }     
+    /*sorting loop finished*/        
+    /*now select the NumNeighb more prominent weights and store into arrays */     
+    for(x=0; x < NumNeighb; x++) {
+        index2 = (N*M*x) + index;
+        H_i_d[index2] = ind_i[x];
+        H_j_d[index2] = ind_j[x];
+        Weights_d[index2] = Weight_Vec[x];
+    }                     
+}
+__global__ void IndexSelect2D_11_kernel(float *Ad, unsigned short *H_i_d, unsigned short *H_j_d, float *Weights_d, float *Eucl_Vec_d, int N, int M, int SearchWindow, int SearchW_full, int SimilarWin, int NumNeighb, float h2)
+{          
+
+    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, counterG, index2;
+    float normsum;
+    
+    float Weight_Vec[CONSTVECSIZE11];
+    unsigned short ind_i[CONSTVECSIZE11];
+    unsigned short ind_j[CONSTVECSIZE11];
+
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    long index = i*M+j;      
+    
+    counter = 0;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            i1 = i+i_m;
+            j1 = j+j_m;
+            if (((i1 >= 0) && (i1 < N)) && ((j1 >= 0) && (j1 < M))) {
+                normsum = 0.0f; counterG = 0;
+                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                        i2 = i1 + i_c;
+                        j2 = j1 + j_c;
+                        i3 = i + i_c;
+                        j3 = j + j_c;
+                        if (((i2 >= 0) && (i2 < N)) && ((j2 >= 0) && (j2 < M))) {
+                            if (((i3 >= 0) && (i3 < N)) && ((j3 >= 0) && (j3 < M))) {
+                                normsum += Eucl_Vec_d[counterG]*powf(Ad[i3*M + j3] - Ad[i2*M + j2], 2);                                
+                                counterG++;
+                            }}                        
+                     }}
+                /* writing temporarily into vectors */
+                if (normsum > EPS) {
+                    Weight_Vec[counter] = __expf(-normsum/h2);
+                    ind_i[counter] = i1;
+                    ind_j[counter] = j1;
+                    counter++;
+                }
+           }
+        }}
+        
+    /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter-1; x++)  {
+       for (y = 0; y < counter-x-1; y++)  {
+           if (Weight_Vec[y] < Weight_Vec[y+1]) {
+            swap(&Weight_Vec[y], &Weight_Vec[y+1]); 		                       
+            swapUS(&ind_i[y], &ind_i[y+1]);
+            swapUS(&ind_j[y], &ind_j[y+1]);  
+            }
+    	}
+    }     
+    /*sorting loop finished*/        
+    /*now select the NumNeighb more prominent weights and store into arrays */     
+    for(x=0; x < NumNeighb; x++) {
+        index2 = (N*M*x) + index;
+        H_i_d[index2] = ind_i[x];
+        H_j_d[index2] = ind_j[x];
+        Weights_d[index2] = Weight_Vec[x];
+    }
+} 
+__global__ void IndexSelect2D_13_kernel(float *Ad, unsigned short *H_i_d, unsigned short *H_j_d, float *Weights_d, float *Eucl_Vec_d, int N, int M, int SearchWindow, int SearchW_full, int SimilarWin, int NumNeighb, float h2)
+{          
+
+    long i1, j1, i_m, j_m, i_c, j_c, i2, j2, i3, j3, counter, x, y, counterG, index2;
+    float normsum;
+    
+    float Weight_Vec[CONSTVECSIZE13];
+    unsigned short ind_i[CONSTVECSIZE13];
+    unsigned short ind_j[CONSTVECSIZE13];
+
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    long index = i*M+j;      
+    
+    counter = 0;
+    for(i_m=-SearchWindow; i_m<=SearchWindow; i_m++) {
+        for(j_m=-SearchWindow; j_m<=SearchWindow; j_m++) {
+            i1 = i+i_m;
+            j1 = j+j_m;
+            if (((i1 >= 0) && (i1 < N)) && ((j1 >= 0) && (j1 < M))) {
+                normsum = 0.0f; counterG = 0;
+                for(i_c=-SimilarWin; i_c<=SimilarWin; i_c++) {
+                    for(j_c=-SimilarWin; j_c<=SimilarWin; j_c++) {
+                        i2 = i1 + i_c;
+                        j2 = j1 + j_c;
+                        i3 = i + i_c;
+                        j3 = j + j_c;
+                        if (((i2 >= 0) && (i2 < N)) && ((j2 >= 0) && (j2 < M))) {
+                            if (((i3 >= 0) && (i3 < N)) && ((j3 >= 0) && (j3 < M))) {
+                                normsum += Eucl_Vec_d[counterG]*powf(Ad[i3*M + j3] - Ad[i2*M + j2], 2);                                
+                                counterG++;
+                            }}                        
+                     }}
+                /* writing temporarily into vectors */
+                if (normsum > EPS) {
+                    Weight_Vec[counter] = __expf(-normsum/h2);
+                    ind_i[counter] = i1;
+                    ind_j[counter] = j1;
+                    counter++;
+                }
+             }
+        }}
+        
+    /* do sorting to choose the most prominent weights [HIGH to LOW] */
+    /* and re-arrange indeces accordingly */
+    for (x = 0; x < counter-1; x++)  {
+       for (y = 0; y < counter-x-1; y++)  {
+           if (Weight_Vec[y] < Weight_Vec[y+1]) {
+            swap(&Weight_Vec[y], &Weight_Vec[y+1]); 		                       
+            swapUS(&ind_i[y], &ind_i[y+1]);
+            swapUS(&ind_j[y], &ind_j[y+1]);  
+            }
+    	}
+    }     
+    /*sorting loop finished*/        
+    /*now select the NumNeighb more prominent weights and store into arrays */     
+    for(x=0; x < NumNeighb; x++) {
+        index2 = (N*M*x) + index;
+        H_i_d[index2] = ind_i[x];
+        H_j_d[index2] = ind_j[x];
+        Weights_d[index2] = Weight_Vec[x];
+    }
+} 
+
+   
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+/********************* MAIN HOST FUNCTION ******************/
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+extern "C" int PatchSelect_GPU_main(float *A, unsigned short *H_i, unsigned short *H_j, float *Weights, int N, int M, int SearchWindow, int SimilarWin, int NumNeighb, float h)
+{
+    int deviceCount = -1; // number of devices
+    cudaGetDeviceCount(&deviceCount);
+    if (deviceCount == 0) {
+        fprintf(stderr, "No CUDA devices found\n");
+        return -1;
+    }  
+      
+    int SearchW_full, SimilW_full, counterG, i, j;
+    float *Ad, *Weights_d, h2, *Eucl_Vec, *Eucl_Vec_d;    
+    unsigned short *H_i_d, *H_j_d;
+    h2 = h*h;
+    
+    dim3 dimBlock(BLKXSIZE,BLKYSIZE);
+    dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE));    
+       
+    SearchW_full = (2*SearchWindow + 1)*(2*SearchWindow + 1); /* the full searching window  size */
+    SimilW_full = (2*SimilarWin + 1)*(2*SimilarWin + 1);   /* the full similarity window  size */
+    
+    /* generate a 2D Gaussian kernel for NLM procedure */
+    Eucl_Vec = (float*) calloc (SimilW_full,sizeof(float));
+    counterG = 0;
+    for(i=-SimilarWin; i<=SimilarWin; i++) {
+         for(j=-SimilarWin; j<=SimilarWin; j++) {
+              Eucl_Vec[counterG] = (float)exp(-(pow(((float) i), 2) + pow(((float) j), 2))/(2.0*SimilarWin*SimilarWin));
+              counterG++;
+    }} /*main neighb loop */
+    
+    
+    /*allocate space on the device*/
+    checkCudaErrors( cudaMalloc((void**)&Ad, N*M*sizeof(float)) );
+    checkCudaErrors( cudaMalloc((void**)&H_i_d, N*M*NumNeighb*sizeof(unsigned short)) );
+    checkCudaErrors( cudaMalloc((void**)&H_j_d, N*M*NumNeighb*sizeof(unsigned short)) );
+    checkCudaErrors( cudaMalloc((void**)&Weights_d, N*M*NumNeighb*sizeof(float)) );
+    checkCudaErrors( cudaMalloc((void**)&Eucl_Vec_d, SimilW_full*sizeof(float)) );
+
+    /* copy data from the host to the device */
+    checkCudaErrors( cudaMemcpy(Ad,A,N*M*sizeof(float),cudaMemcpyHostToDevice) );
+    checkCudaErrors( cudaMemcpy(Eucl_Vec_d,Eucl_Vec,SimilW_full*sizeof(float),cudaMemcpyHostToDevice) );    
+ 
+    /********************** Run CUDA kernel here ********************/
+    if (SearchWindow == 5)  IndexSelect2D_5_kernel<<<dimGrid,dimBlock>>>(Ad, H_i_d, H_j_d, Weights_d, Eucl_Vec_d, N, M, SearchWindow, SearchW_full, SimilarWin, NumNeighb, h2);
+    else if (SearchWindow == 7)  IndexSelect2D_7_kernel<<<dimGrid,dimBlock>>>(Ad, H_i_d, H_j_d, Weights_d, Eucl_Vec_d, N, M, SearchWindow, SearchW_full, SimilarWin, NumNeighb, h2);
+    else if (SearchWindow == 9)  IndexSelect2D_9_kernel<<<dimGrid,dimBlock>>>(Ad, H_i_d, H_j_d, Weights_d, Eucl_Vec_d, N, M, SearchWindow, SearchW_full, SimilarWin, NumNeighb, h2);
+    else if (SearchWindow == 11)  IndexSelect2D_11_kernel<<<dimGrid,dimBlock>>>(Ad, H_i_d, H_j_d, Weights_d, Eucl_Vec_d, N, M, SearchWindow, SearchW_full, SimilarWin, NumNeighb, h2);
+    else if (SearchWindow == 13)  IndexSelect2D_13_kernel<<<dimGrid,dimBlock>>>(Ad, H_i_d, H_j_d, Weights_d, Eucl_Vec_d, N, M, SearchWindow, SearchW_full, SimilarWin, NumNeighb, h2);
+    else {
+    fprintf(stderr, "Select the searching window size from 5, 7, 9, 11 or 13\n");
+        return -1;}    
+    checkCudaErrors(cudaPeekAtLastError() );        
+    checkCudaErrors(cudaDeviceSynchronize());   
+    /***************************************************************/    
+        
+    checkCudaErrors(cudaMemcpy(H_i, H_i_d, N*M*NumNeighb*sizeof(unsigned short),cudaMemcpyDeviceToHost) );
+    checkCudaErrors(cudaMemcpy(H_j, H_j_d, N*M*NumNeighb*sizeof(unsigned short),cudaMemcpyDeviceToHost) );  
+    checkCudaErrors(cudaMemcpy(Weights, Weights_d, N*M*NumNeighb*sizeof(float),cudaMemcpyDeviceToHost) );   
+    
+    
+    cudaFree(Ad); 
+    cudaFree(H_i_d); 
+    cudaFree(H_j_d);    
+    cudaFree(Weights_d);
+    cudaFree(Eucl_Vec_d);
+    cudaDeviceReset();
+    return 0;
+}
diff --git a/src/Core/regularisers_GPU/PatchSelect_GPU_core.h b/src/Core/regularisers_GPU/PatchSelect_GPU_core.h
new file mode 100644
index 0000000..8c124d3
--- /dev/null
+++ b/src/Core/regularisers_GPU/PatchSelect_GPU_core.h
@@ -0,0 +1,8 @@
+#ifndef __NLREG_KERNELS_H_
+#define __NLREG_KERNELS_H_
+#include "CCPiDefines.h"
+#include <stdio.h>
+
+extern "C" CCPI_EXPORT int PatchSelect_GPU_main(float *A, unsigned short *H_i, unsigned short *H_j, float *Weights, int N, int M, int SearchWindow, int SimilarWin, int NumNeighb, float h);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/TGV_GPU_core.cu b/src/Core/regularisers_GPU/TGV_GPU_core.cu
new file mode 100644
index 0000000..58b2c41
--- /dev/null
+++ b/src/Core/regularisers_GPU/TGV_GPU_core.cu
@@ -0,0 +1,625 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "TGV_GPU_core.h"
+#include "shared.h"
+
+/* CUDA implementation of Primal-Dual denoising method for 
+ * Total Generilized Variation (TGV)-L2 model [1] (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume (2D/3D)
+ * 2. lambda - regularisation parameter
+ * 3. parameter to control the first-order term (alpha1)
+ * 4. parameter to control the second-order term (alpha0)
+ * 5. Number of Chambolle-Pock (Primal-Dual) iterations
+ * 6. Lipshitz constant (default is 12)
+ *
+ * Output:
+ * Filtered/regulariaed image 
+ *
+ * References:
+ * [1] K. Bredies "Total Generalized Variation"
+ */
+    
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8    
+    
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+#define EPS 1.0e-7
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+
+
+/********************************************************************/
+/***************************2D Functions*****************************/
+/********************************************************************/
+__global__ void DualP_2D_kernel(float *U, float *V1, float *V2, float *P1, float *P2, int dimX, int dimY, float sigma)
+{    
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            /* symmetric boundary conditions (Neuman) */
+            if (i == dimX-1) P1[index] += sigma*((U[j*dimX+(i-1)] - U[index]) - V1[index]); 
+            else P1[index] += sigma*((U[j*dimX+(i+1)] - U[index])  - V1[index]); 
+            if (j == dimY-1) P2[index] += sigma*((U[(j-1)*dimX+i] - U[index])  - V2[index]);
+            else  P2[index] += sigma*((U[(j+1)*dimX+i] - U[index])  - V2[index]);
+		}
+	return;
+} 
+
+__global__ void ProjP_2D_kernel(float *P1, float *P2, int dimX, int dimY, float alpha1)
+{
+   	float grad_magn;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+            
+            grad_magn = sqrt(pow(P1[index],2) + pow(P2[index],2));
+            grad_magn = grad_magn/alpha1;
+            if (grad_magn > 1.0f) {
+                P1[index] /= grad_magn;
+                P2[index] /= grad_magn;
+            }
+		}
+	return;
+} 
+
+__global__ void DualQ_2D_kernel(float *V1, float *V2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, float sigma)
+{
+        float q1, q2, q11, q22;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {            
+            /* symmetric boundary conditions (Neuman) */
+    	    q1 = 0.0f; q11 = 0.0f; q2 = 0.0f; q22 = 0.0f;
+            /* boundary conditions (Neuman) */
+            if (i != dimX-1){
+                q1 = V1[j*dimX+(i+1)] - V1[index];
+                q11 = V2[j*dimX+(i+1)] - V2[index];
+            }
+            if (j != dimY-1) {
+                q2 = V2[(j+1)*dimX+i] - V2[index];
+                q22 = V1[(j+1)*dimX+i] - V1[index];
+            }
+            Q1[index] += sigma*(q1);
+            Q2[index] += sigma*(q2);
+            Q3[index] += sigma*(0.5f*(q11 + q22));
+	}
+	return;
+} 
+
+__global__ void ProjQ_2D_kernel(float *Q1, float *Q2, float *Q3, int dimX, int dimY, float alpha0)
+{
+	float grad_magn;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {            
+            grad_magn = sqrt(pow(Q1[index],2) + pow(Q2[index],2) + 2*pow(Q3[index],2));
+            grad_magn = grad_magn/alpha0;
+            if (grad_magn > 1.0f) {
+                Q1[index] /= grad_magn;
+                Q2[index] /= grad_magn;
+                Q3[index] /= grad_magn;
+        	    }
+	}
+	return;
+} 
+
+__global__ void DivProjP_2D_kernel(float *U, float *U0, float *P1, float *P2, int dimX, int dimY, float lambda, float tau)
+{
+	float P_v1, P_v2, div;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {
+			
+            if (i == 0) P_v1 = P1[index];
+            else P_v1 = P1[index] - P1[j*dimX+(i-1)];
+            if (j == 0) P_v2 = P2[index];
+            else  P_v2 = P2[index] - P2[(j-1)*dimX+i];
+            div = P_v1 + P_v2;
+            U[index] = (lambda*(U[index] + tau*div) + tau*U0[index])/(lambda + tau);
+		}
+	return;
+} 
+
+__global__ void UpdV_2D_kernel(float *V1, float *V2, float *P1, float *P2, float *Q1, float *Q2, float *Q3, int dimX, int dimY, float tau)
+{
+	float q1, q3_x, q2, q3_y, div1, div2;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + dimX*j;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY)) {			
+   	    q2 = 0.0f;  q3_y = 0.0f; q1 = 0.0f; q3_x = 0.0;
+            /* boundary conditions (Neuman) */
+            if (i != 0) {
+                q1 = Q1[index] - Q1[j*dimX+(i-1)];
+                q3_x = Q3[index] - Q3[j*dimX+(i-1)];
+            }
+            if (j != 0) {
+                q2 = Q2[index] - Q2[(j-1)*dimX+i];
+                q3_y = Q3[index] - Q3[(j-1)*dimX+i];
+            }
+            div1 = q1 + q3_y;
+            div2 = q3_x + q2;
+            V1[index] += tau*(P1[index] + div1);
+            V2[index] += tau*(P2[index] + div2);
+	}
+	return;
+} 
+
+__global__ void copyIm_TGV_kernel(float *U, float *U_old, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)   {
+        U_old[index] = U[index];
+    }
+}
+
+__global__ void copyIm_TGV_kernel_ar2(float *V1, float *V2, float *V1_old, float *V2_old, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)   {
+        V1_old[index] = V1[index];
+        V2_old[index] = V2[index];
+    }
+}
+
+__global__ void newU_kernel(float *U, float *U_old, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        U[index] = 2.0f*U[index] - U_old[index];
+    }
+}
+
+
+__global__ void newU_kernel_ar2(float *V1, float *V2, float *V1_old, float *V2_old, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        V1[index] = 2.0f*V1[index] - V1_old[index];
+        V2[index] = 2.0f*V2[index] - V2_old[index];  
+    }
+}
+/********************************************************************/
+/***************************3D Functions*****************************/
+/********************************************************************/
+__global__ void DualP_3D_kernel(float *U, float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float sigma)
+{    
+	int index;
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+	
+	    index = (dimX*dimY)*k + j*dimX+i;
+            /* symmetric boundary conditions (Neuman) */
+            if (i == dimX-1) P1[index] += sigma*((U[(dimX*dimY)*k + j*dimX+(i-1)] - U[index]) - V1[index]); 
+            else P1[index] += sigma*((U[(dimX*dimY)*k + j*dimX+(i+1)] - U[index])  - V1[index]); 
+            if (j == dimY-1) P2[index] += sigma*((U[(dimX*dimY)*k + (j-1)*dimX+i] - U[index])  - V2[index]);
+            else  P2[index] += sigma*((U[(dimX*dimY)*k + (j+1)*dimX+i] - U[index])  - V2[index]);
+            if (k == dimZ-1) P3[index] += sigma*((U[(dimX*dimY)*(k-1) + j*dimX+i] - U[index])  - V3[index]);
+            else  P3[index] += sigma*((U[(dimX*dimY)*(k+1) + j*dimX+i] - U[index])  - V3[index]);
+	}
+	return;
+} 
+
+__global__ void ProjP_3D_kernel(float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float alpha1)
+{
+   	float grad_magn;
+   	int index;
+   	
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {	
+	    index = (dimX*dimY)*k + j*dimX+i;
+            
+            grad_magn = (sqrtf(pow(P1[index],2) + pow(P2[index],2) + pow(P3[index],2)))/alpha1;
+            if (grad_magn > 1.0f) {
+                P1[index] /= grad_magn;
+                P2[index] /= grad_magn;
+                P3[index] /= grad_magn;
+            }
+	}
+	return;
+}
+
+__global__ void DualQ_3D_kernel(float *V1, float *V2, float *V3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, int dimX, int dimY, int dimZ, float sigma)
+{
+	int index; 
+        float q1, q2, q3, q11, q22, q33, q44, q55, q66;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {	
+	    
+	    index = (dimX*dimY)*k + j*dimX+i;	    
+	    q1 = 0.0f; q11 = 0.0f; q33 = 0.0f; q2 = 0.0f; q22 = 0.0f; q55 = 0.0f; q3 = 0.0f; q44 = 0.0f; q66 = 0.0f;
+            /* symmetric boundary conditions (Neuman) */
+            if (i != dimX-1){ 
+                q1 = V1[(dimX*dimY)*k + j*dimX+(i+1)] - V1[index];              
+                q11 = V2[(dimX*dimY)*k + j*dimX+(i+1)] - V2[index];
+                q33 = V3[(dimX*dimY)*k + j*dimX+(i+1)] - V3[index];
+            }
+            if (j != dimY-1) {
+                q2 = V2[(dimX*dimY)*k + (j+1)*dimX+i] - V2[index];                
+                q22 = V1[(dimX*dimY)*k + (j+1)*dimX+i] - V1[index];
+                q55 = V3[(dimX*dimY)*k + (j+1)*dimX+i] - V3[index];
+            }
+            if (k != dimZ-1) {
+                q3 = V3[(dimX*dimY)*(k+1) + j*dimX+i] - V3[index];
+                q44 = V1[(dimX*dimY)*(k+1) + j*dimX+i] - V1[index];
+                q66 = V2[(dimX*dimY)*(k+1) + j*dimX+i] - V2[index];
+            }
+            
+            Q1[index] += sigma*(q1); /*Q11*/
+            Q2[index] += sigma*(q2); /*Q22*/            
+            Q3[index] += sigma*(q3); /*Q33*/
+            Q4[index] += sigma*(0.5f*(q11 + q22)); /* Q21 / Q12 */
+            Q5[index] += sigma*(0.5f*(q33 + q44)); /* Q31 / Q13 */
+            Q6[index] += sigma*(0.5f*(q55 + q66)); /* Q32 / Q23 */
+	}
+	return;
+}
+
+
+__global__ void ProjQ_3D_kernel(float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, int dimX, int dimY, int dimZ, float alpha0)
+{
+	float grad_magn;
+	int index;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {	
+	    
+        index = (dimX*dimY)*k + j*dimX+i;	
+	
+	grad_magn = sqrtf(pow(Q1[index],2) + pow(Q2[index],2) + pow(Q3[index],2) + 2.0f*pow(Q4[index],2) + 2.0f*pow(Q5[index],2) + 2.0f*pow(Q6[index],2));
+            grad_magn = grad_magn/alpha0;
+            if (grad_magn > 1.0f) {
+                Q1[index] /= grad_magn;
+                Q2[index] /= grad_magn;
+                Q3[index] /= grad_magn;
+                Q4[index] /= grad_magn;
+                Q5[index] /= grad_magn;
+                Q6[index] /= grad_magn;
+            }
+	}
+	return;
+} 
+__global__ void DivProjP_3D_kernel(float *U, float *U0, float *P1, float *P2, float *P3, int dimX, int dimY, int dimZ, float lambda, float tau)
+{
+	float P_v1, P_v2, P_v3, div;
+	int index;
+
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {	
+
+        index = (dimX*dimY)*k + j*dimX+i;	
+			
+        if (i == 0) P_v1 = P1[index];
+        else P_v1 = P1[index] - P1[(dimX*dimY)*k + j*dimX+(i-1)];
+        if (j == 0) P_v2 = P2[index];
+        else P_v2 = P2[index] - P2[(dimX*dimY)*k + (j-1)*dimX+i];
+        if (k == 0) P_v3 = P3[index];
+        else P_v3 = P3[index] - P3[(dimX*dimY)*(k-1) + (j)*dimX+i];              
+                      
+        div = P_v1 + P_v2 + P_v3;
+        U[index] = (lambda*(U[index] + tau*div) + tau*U0[index])/(lambda + tau);             
+	}
+	return;
+}
+__global__ void UpdV_3D_kernel(float *V1, float *V2, float *V3, float *P1, float *P2, float *P3, float *Q1, float *Q2, float *Q3, float *Q4, float *Q5, float *Q6, int dimX, int dimY, int dimZ, float tau)
+{
+	float q1, q4x, q5x, q2, q4y, q6y, q6z, q5z, q3, div1, div2, div3;
+	int index;
+	
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {	
+
+        index = (dimX*dimY)*k + j*dimX+i;	
+        
+	q1 = 0.0f; q4x= 0.0f; q5x= 0.0f; q2= 0.0f; q4y= 0.0f; q6y= 0.0f; q6z= 0.0f; q5z= 0.0f; q3= 0.0f;
+        /* Q1 - Q11, Q2 - Q22, Q3 -  Q33, Q4 - Q21/Q12, Q5 - Q31/Q13, Q6 - Q32/Q23*/            
+        /* symmetric boundary conditions (Neuman) */
+        if (i != 0) {
+                q1 = Q1[index] - Q1[(dimX*dimY)*k + j*dimX+(i-1)];
+                q4x = Q4[index] - Q4[(dimX*dimY)*k + j*dimX+(i-1)];                
+                q5x = Q5[index] - Q5[(dimX*dimY)*k + j*dimX+(i-1)];
+        }
+       if (j != 0) {
+                q2 = Q2[index] - Q2[(dimX*dimY)*k + (j-1)*dimX+i];
+                q4y = Q4[index] - Q4[(dimX*dimY)*k + (j-1)*dimX+i];
+                q6y = Q6[index] - Q6[(dimX*dimY)*k + (j-1)*dimX+i];
+       }
+       if (k != 0) {
+                q6z = Q6[index] - Q6[(dimX*dimY)*(k-1) + (j)*dimX+i];
+                q5z = Q5[index] - Q5[(dimX*dimY)*(k-1) + (j)*dimX+i];
+                q3 = Q3[index] - Q3[(dimX*dimY)*(k-1) + (j)*dimX+i];
+       }
+       div1 = q1 + q4y + q5z;
+       div2 = q4x + q2 + q6z;            
+       div3 = q5x + q6y + q3;
+            
+        V1[index] += tau*(P1[index] + div1);
+        V2[index] += tau*(P2[index] + div2);
+        V3[index] += tau*(P3[index] + div3);
+	}
+	return;
+} 
+
+__global__ void copyIm_TGV_kernel3D(float *U, float *U_old, int dimX, int dimY, int dimZ, int num_total)
+{
+    int index;
+	
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;    
+    
+    index = (dimX*dimY)*k + j*dimX+i;
+    
+    if (index < num_total) {	
+      	U_old[index] = U[index];	
+    }
+}
+
+__global__ void copyIm_TGV_kernel3D_ar3(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, int dimX, int dimY, int dimZ, int num_total)
+{
+    int index;
+	
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;    
+    
+    index = (dimX*dimY)*k + j*dimX+i;
+    
+    if (index < num_total) {	
+      	V1_old[index] = V1[index];
+	V2_old[index] = V2[index];
+	V3_old[index] = V3[index];	
+    }
+}
+
+__global__ void newU_kernel3D(float *U, float *U_old, int dimX, int dimY, int dimZ, int num_total)
+{
+     int index;
+	
+     int i = blockDim.x * blockIdx.x + threadIdx.x;
+     int j = blockDim.y * blockIdx.y + threadIdx.y;
+     int k = blockDim.z * blockIdx.z + threadIdx.z;    
+         
+     index = (dimX*dimY)*k + j*dimX+i;
+    
+    if (index < num_total) {
+	   U[index] = 2.0f*U[index] - U_old[index];
+    }
+}  
+
+__global__ void newU_kernel3D_ar3(float *V1, float *V2, float *V3, float *V1_old, float *V2_old, float *V3_old, int dimX, int dimY, int dimZ, int num_total)
+{
+     int index;
+	
+     int i = blockDim.x * blockIdx.x + threadIdx.x;
+     int j = blockDim.y * blockIdx.y + threadIdx.y;
+     int k = blockDim.z * blockIdx.z + threadIdx.z;    
+         
+     index = (dimX*dimY)*k + j*dimX+i;
+    
+    if (index < num_total) {
+	   V1[index] = 2.0f*V1[index] - V1_old[index];
+	   V2[index] = 2.0f*V2[index] - V2_old[index];
+	   V3[index] = 2.0f*V3[index] - V3_old[index];
+    }
+}  
+
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+/************************ MAIN HOST FUNCTION ***********************/
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+extern "C" int TGV_GPU_main(float *U0, float *U, float lambda, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ)
+{
+	int dimTotal, dev = 0;
+	CHECK(cudaSetDevice(dev));
+	
+	dimTotal = dimX*dimY*dimZ;
+       
+        float *U_old, *d_U0, *d_U, *P1, *P2, *Q1, *Q2, *Q3, *V1, *V1_old, *V2, *V2_old, tau, sigma;
+        tau = pow(L2,-0.5);
+        sigma = pow(L2,-0.5);
+                                      
+        CHECK(cudaMalloc((void**)&d_U0,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_U,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&U_old,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&P1,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&P2,dimTotal*sizeof(float)));
+        
+        CHECK(cudaMalloc((void**)&Q1,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&Q2,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&Q3,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&V1,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&V2,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&V1_old,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&V2_old,dimTotal*sizeof(float)));
+        
+        CHECK(cudaMemcpy(d_U0,U0,dimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(d_U,U0,dimTotal*sizeof(float),cudaMemcpyHostToDevice));      
+        
+        if (dimZ == 1) {
+	/*2D case */
+        dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+        dim3 dimGrid(idivup(dimX,BLKXSIZE2D), idivup(dimY,BLKYSIZE2D));
+             
+        for(int n=0; n < iterationsNumb; n++) {
+			
+	    /* Calculate Dual Variable P */
+            DualP_2D_kernel<<<dimGrid,dimBlock>>>(d_U, V1, V2, P1, P2, dimX, dimY, sigma);
+	    CHECK(cudaDeviceSynchronize());
+            /*Projection onto convex set for P*/
+            ProjP_2D_kernel<<<dimGrid,dimBlock>>>(P1, P2, dimX, dimY, alpha1);
+            CHECK(cudaDeviceSynchronize());
+            /* Calculate Dual Variable Q */
+            DualQ_2D_kernel<<<dimGrid,dimBlock>>>(V1, V2, Q1, Q2, Q3, dimX, dimY, sigma);
+            CHECK(cudaDeviceSynchronize());
+             /*Projection onto convex set for Q*/
+            ProjQ_2D_kernel<<<dimGrid,dimBlock>>>(Q1, Q2, Q3, dimX, dimY, alpha0);
+            CHECK(cudaDeviceSynchronize());
+            /*saving U into U_old*/
+            copyIm_TGV_kernel<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimTotal);
+            CHECK(cudaDeviceSynchronize());
+            /*adjoint operation  -> divergence and projection of P*/
+            DivProjP_2D_kernel<<<dimGrid,dimBlock>>>(d_U, d_U0, P1, P2, dimX, dimY, lambda, tau);
+            CHECK(cudaDeviceSynchronize());
+            /*get updated solution U*/
+            newU_kernel<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimTotal);
+            CHECK(cudaDeviceSynchronize());
+            /*saving V into V_old*/
+            copyIm_TGV_kernel_ar2<<<dimGrid,dimBlock>>>(V1, V2, V1_old, V2_old, dimX, dimY, dimTotal);
+            CHECK(cudaDeviceSynchronize());
+            /* upd V*/
+            UpdV_2D_kernel<<<dimGrid,dimBlock>>>(V1, V2, P1, P2, Q1, Q2, Q3, dimX, dimY, tau);
+            CHECK(cudaDeviceSynchronize());
+            /*get new V*/
+            newU_kernel_ar2<<<dimGrid,dimBlock>>>(V1, V2, V1_old, V2_old, dimX, dimY, dimTotal);
+            CHECK(cudaDeviceSynchronize());            
+	        }
+        }
+        else {
+        /*3D case */
+        dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+        dim3 dimGrid(idivup(dimX,BLKXSIZE), idivup(dimY,BLKYSIZE),idivup(dimZ,BLKXSIZE));
+        
+        float *P3, *Q4, *Q5, *Q6, *V3, *V3_old;
+        
+	CHECK(cudaMalloc((void**)&P3,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&Q4,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&Q5,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&Q6,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&V3,dimTotal*sizeof(float)));
+        CHECK(cudaMalloc((void**)&V3_old,dimTotal*sizeof(float)));
+        
+        for(int n=0; n < iterationsNumb; n++) {
+			
+	    /* Calculate Dual Variable P */
+            DualP_3D_kernel<<<dimGrid,dimBlock>>>(d_U, V1, V2, V3, P1, P2, P3, dimX, dimY, dimZ, sigma);
+	    CHECK(cudaDeviceSynchronize());
+            /*Projection onto convex set for P*/
+            ProjP_3D_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, dimX, dimY, dimZ, alpha1);
+            CHECK(cudaDeviceSynchronize());
+            /* Calculate Dual Variable Q */
+            DualQ_3D_kernel<<<dimGrid,dimBlock>>>(V1, V2, V3, Q1, Q2, Q3, Q4, Q5, Q6, dimX, dimY, dimZ, sigma);
+            CHECK(cudaDeviceSynchronize());
+             /*Projection onto convex set for Q*/
+            ProjQ_3D_kernel<<<dimGrid,dimBlock>>>(Q1, Q2, Q3, Q4, Q5, Q6, dimX, dimY, dimZ, alpha0);
+            CHECK(cudaDeviceSynchronize());
+            /*saving U into U_old*/
+            copyIm_TGV_kernel3D<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimZ, dimTotal);
+            CHECK(cudaDeviceSynchronize());
+            /*adjoint operation  -> divergence and projection of P*/
+            DivProjP_3D_kernel<<<dimGrid,dimBlock>>>(d_U, d_U0, P1, P2, P3, dimX, dimY, dimZ, lambda, tau);
+            CHECK(cudaDeviceSynchronize());
+            /*get updated solution U*/
+            newU_kernel3D<<<dimGrid,dimBlock>>>(d_U, U_old, dimX, dimY, dimZ, dimTotal);
+            CHECK(cudaDeviceSynchronize());
+            /*saving V into V_old*/
+            copyIm_TGV_kernel3D_ar3<<<dimGrid,dimBlock>>>(V1, V2, V3, V1_old, V2_old, V3_old, dimX, dimY, dimZ, dimTotal);           
+            CHECK(cudaDeviceSynchronize());
+            /* upd V*/
+            UpdV_3D_kernel<<<dimGrid,dimBlock>>>(V1, V2, V3, P1, P2, P3, Q1, Q2, Q3, Q4, Q5, Q6, dimX, dimY, dimZ, tau);
+            CHECK(cudaDeviceSynchronize());
+            /*get new V*/
+            newU_kernel3D_ar3<<<dimGrid,dimBlock>>>(V1, V2, V3, V1_old, V2_old, V3_old, dimX, dimY, dimZ, dimTotal);
+            CHECK(cudaDeviceSynchronize());            
+	        }
+	        
+        CHECK(cudaFree(Q4));
+        CHECK(cudaFree(Q5));
+        CHECK(cudaFree(Q6));
+        CHECK(cudaFree(P3));
+        CHECK(cudaFree(V3));
+        CHECK(cudaFree(V3_old));	                
+        }
+        
+        CHECK(cudaMemcpy(U,d_U,dimTotal*sizeof(float),cudaMemcpyDeviceToHost));
+        CHECK(cudaFree(d_U0));
+        CHECK(cudaFree(d_U));
+        CHECK(cudaFree(U_old));
+        CHECK(cudaFree(P1));
+        CHECK(cudaFree(P2));
+        
+        CHECK(cudaFree(Q1));
+        CHECK(cudaFree(Q2));
+        CHECK(cudaFree(Q3));
+        CHECK(cudaFree(V1));
+        CHECK(cudaFree(V2));
+        CHECK(cudaFree(V1_old));
+        CHECK(cudaFree(V2_old));
+        return 0;
+}
diff --git a/src/Core/regularisers_GPU/TGV_GPU_core.h b/src/Core/regularisers_GPU/TGV_GPU_core.h
new file mode 100644
index 0000000..9f73d1c
--- /dev/null
+++ b/src/Core/regularisers_GPU/TGV_GPU_core.h
@@ -0,0 +1,8 @@
+#ifndef __TGV_GPU_H__
+#define __TGV_GPU_H__
+#include "CCPiDefines.h"
+#include <stdio.h>
+
+extern "C" CCPI_EXPORT int TGV_GPU_main(float *U0, float *U, float lambda, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/TV_FGP_GPU_core.cu b/src/Core/regularisers_GPU/TV_FGP_GPU_core.cu
new file mode 100755
index 0000000..b371c5d
--- /dev/null
+++ b/src/Core/regularisers_GPU/TV_FGP_GPU_core.cu
@@ -0,0 +1,564 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "TV_FGP_GPU_core.h"
+#include "shared.h"
+#include <thrust/device_vector.h>
+#include <thrust/transform_reduce.h>
+
+/* CUDA implementation of FGP-TV [1] denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambdaPar - regularization parameter 
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant 
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default) 
+ * 7. print information: 0 (off) or 1 (on) 
+ *
+ * Output:
+ * [1] Filtered/regularized image
+ *
+ * This function is based on the Matlab's code and paper by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ */
+
+
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+struct square { __host__ __device__ float operator()(float x) { return x * x; } };
+
+/************************************************/
+/*****************2D modules*********************/
+/************************************************/
+__global__ void Obj_func2D_kernel(float *Ad, float *D, float *R1, float *R2, int N, int M, int ImSize, float lambda)
+{
+    
+    float val1,val2;
+    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex; 
+    
+    if ((xIndex < N) && (yIndex < M)) {        
+        if (xIndex <= 0) {val1 = 0.0f;} else {val1 = R1[(xIndex-1) + N*yIndex];}
+        if (yIndex <= 0) {val2 = 0.0f;} else {val2 = R2[xIndex + N*(yIndex-1)];}
+        //Write final result to global memory
+        D[index] = Ad[index] - lambda*(R1[index] + R2[index] - val1 - val2);
+    }
+    return;
+}
+
+__global__ void Grad_func2D_kernel(float *P1, float *P2, float *D, float *R1, float *R2, int N, int M, int ImSize, float multip)
+{
+    
+    float val1,val2;
+    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) {        
+        
+        /* boundary conditions */
+        if (xIndex >= N-1) val1 = 0.0f; else val1 = D[index] - D[(xIndex+1) + N*yIndex];
+        if (yIndex >= M-1) val2 = 0.0f; else val2 = D[index] - D[(xIndex) + N*(yIndex + 1)];
+        
+        //Write final result to global memory
+        P1[index] = R1[index] + multip*val1;
+        P2[index] = R2[index] + multip*val2;
+    }
+    return;
+}
+
+__global__ void Proj_func2D_iso_kernel(float *P1, float *P2, int N, int M, int ImSize)
+{
+    
+    float denom;    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) { 
+        denom = pow(P1[index],2) +  pow(P2[index],2);        
+        if (denom > 1.0f) {
+            P1[index] = P1[index]/sqrt(denom);
+            P2[index] = P2[index]/sqrt(denom);
+        }
+    }
+    return;
+}
+__global__ void Proj_func2D_aniso_kernel(float *P1, float *P2, int N, int M, int ImSize)
+{
+    
+    float val1, val2;    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) { 
+                val1 = abs(P1[index]);
+                val2 = abs(P2[index]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                P1[index] = P1[index]/val1;
+                P2[index] = P2[index]/val2;
+    }
+    return;
+}
+__global__ void Rupd_func2D_kernel(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, float multip2, int N, int M, int ImSize)
+{
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) { 
+        R1[index] = P1[index] + multip2*(P1[index] - P1_old[index]);
+        R2[index] = P2[index] + multip2*(P2[index] - P2_old[index]);
+    }
+    return;
+}
+__global__ void nonneg2D_kernel(float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        if (Output[index] < 0.0f) Output[index] = 0.0f;
+    }
+}
+/************************************************/
+/*****************3D modules*********************/
+/************************************************/
+__global__ void Obj_func3D_kernel(float *Ad, float *D, float *R1, float *R2, float *R3, int N, int M, int Z, int ImSize, float lambda)
+{
+    
+    float val1,val2,val3;
+    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {      
+        if (i <= 0) {val1 = 0.0f;} else {val1 = R1[(N*M)*(k) + (i-1) + N*j];}
+        if (j <= 0) {val2 = 0.0f;} else {val2 = R2[(N*M)*(k) + i + N*(j-1)];}
+        if (k <= 0) {val3 = 0.0f;} else {val3 = R3[(N*M)*(k-1) + i + N*j];}
+        //Write final result to global memory
+        D[index] = Ad[index] - lambda*(R1[index] + R2[index] + R3[index] - val1 - val2 - val3);
+    }
+    return;
+}
+
+__global__ void Grad_func3D_kernel(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, int N, int M, int Z, int ImSize, float multip)
+{
+    
+    float val1,val2,val3;
+    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) {       
+        /* boundary conditions */
+        if (i >= N-1) val1 = 0.0f; else val1 = D[index] - D[(N*M)*(k) + (i+1) + N*j];
+        if (j >= M-1) val2 = 0.0f; else val2 = D[index] - D[(N*M)*(k) + i + N*(j+1)];
+        if (k >= Z-1) val3 = 0.0f; else val3 = D[index] - D[(N*M)*(k+1) + i + N*j];
+        
+        //Write final result to global memory
+        P1[index] = R1[index] + multip*val1;
+        P2[index] = R2[index] + multip*val2;
+        P3[index] = R3[index] + multip*val3;
+    }
+    return;
+}
+
+__global__ void Proj_func3D_iso_kernel(float *P1, float *P2, float *P3, int N, int M, int Z, int ImSize)
+{
+    
+    float denom,sq_denom;    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) {
+        denom = pow(P1[index],2) +  pow(P2[index],2) + pow(P3[index],2);
+        
+        if (denom > 1.0f) {
+            sq_denom = 1.0f/sqrt(denom);
+            P1[index] = P1[index]*sq_denom;
+            P2[index] = P2[index]*sq_denom;
+            P3[index] = P3[index]*sq_denom;
+        }
+    }
+    return;
+}
+
+__global__ void Proj_func3D_aniso_kernel(float *P1, float *P2, float *P3, int N, int M, int Z, int ImSize)
+{
+    
+    float val1, val2, val3;    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) {
+                val1 = abs(P1[index]);
+                val2 = abs(P2[index]);
+                val3 = abs(P3[index]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                if (val3 < 1.0f) {val3 = 1.0f;}
+                P1[index] = P1[index]/val1;
+                P2[index] = P2[index]/val2;
+                P3[index] = P3[index]/val3;
+    }
+    return;
+}
+__global__ void Rupd_func3D_kernel(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, float multip2, int N, int M, int Z, int ImSize)
+{
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) { 
+        R1[index] = P1[index] + multip2*(P1[index] - P1_old[index]);
+        R2[index] = P2[index] + multip2*(P2[index] - P2_old[index]);
+        R3[index] = P3[index] + multip2*(P3[index] - P3_old[index]);
+    }
+    return;
+}
+
+__global__ void nonneg3D_kernel(float* Output, int N, int M, int Z, int num_total)
+{
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        if (Output[index] < 0.0f) Output[index] = 0.0f;
+    }
+}
+__global__ void FGPcopy_kernel2D(float *Input, float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        Output[index] = Input[index];
+    }
+}
+
+__global__ void FGPcopy_kernel3D(float *Input, float* Output, int N, int M, int Z, int num_total)
+{
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        Output[index] = Input[index];
+    }
+}
+
+__global__ void FGPResidCalc2D_kernel(float *Input1, float *Input2, float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        Output[index] = Input1[index] - Input2[index];
+    }
+}
+
+__global__ void FGPResidCalc3D_kernel(float *Input1, float *Input2, float* Output, int N, int M, int Z, int num_total)
+{
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        Output[index] = Input1[index] - Input2[index];
+    }
+}
+
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+
+////////////MAIN HOST FUNCTION ///////////////
+extern "C" int TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ)
+{
+    int deviceCount = -1; // number of devices
+    cudaGetDeviceCount(&deviceCount);
+    if (deviceCount == 0) {
+        fprintf(stderr, "No CUDA devices found\n");
+        return -1;
+    }
+    
+    int count = 0, i;
+    float re, multip,multip2;    
+	float tk = 1.0f;
+    float tkp1=1.0f;
+        
+    if (dimZ <= 1) {
+		/*2D verson*/
+		int ImSize = dimX*dimY;    
+		float *d_input, *d_update=NULL, *d_update_prev=NULL, *P1=NULL, *P2=NULL, *P1_prev=NULL, *P2_prev=NULL, *R1=NULL, *R2=NULL;
+   
+		dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+		dim3 dimGrid(idivup(dimX,BLKXSIZE2D), idivup(dimY,BLKYSIZE2D));
+    
+		/*allocate space for images on device*/
+		checkCudaErrors( cudaMalloc((void**)&d_input,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&d_update,ImSize*sizeof(float)) );
+		if (epsil != 0.0f) checkCudaErrors( cudaMalloc((void**)&d_update_prev,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P1,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P2,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P1_prev,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P2_prev,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&R1,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&R2,ImSize*sizeof(float)) );
+    
+        checkCudaErrors( cudaMemcpy(d_input,Input,ImSize*sizeof(float),cudaMemcpyHostToDevice));
+        cudaMemset(P1, 0, ImSize*sizeof(float));
+        cudaMemset(P2, 0, ImSize*sizeof(float));
+        cudaMemset(P1_prev, 0, ImSize*sizeof(float));
+        cudaMemset(P2_prev, 0, ImSize*sizeof(float));
+        cudaMemset(R1, 0, ImSize*sizeof(float));
+        cudaMemset(R2, 0, ImSize*sizeof(float));
+
+        /********************** Run CUDA 2D kernel here ********************/    
+        multip = (1.0f/(8.0f*lambdaPar));
+    
+        /* The main kernel */
+        for (i = 0; i < iter; i++) {
+        
+            /* computing the gradient of the objective function */
+            Obj_func2D_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, R1, R2, dimX, dimY, ImSize, lambdaPar);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+            
+            if (nonneg != 0) {
+            nonneg2D_kernel<<<dimGrid,dimBlock>>>(d_update, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() ); }
+                    
+            /*Taking a step towards minus of the gradient*/
+            Grad_func2D_kernel<<<dimGrid,dimBlock>>>(P1, P2, d_update, R1, R2, dimX, dimY, ImSize, multip);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            /* projection step */
+            if (methodTV == 0) Proj_func2D_iso_kernel<<<dimGrid,dimBlock>>>(P1, P2, dimX, dimY, ImSize); /*isotropic TV*/
+            else Proj_func2D_aniso_kernel<<<dimGrid,dimBlock>>>(P1, P2, dimX, dimY, ImSize); /*anisotropic TV*/            
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            multip2 = ((tk-1.0f)/tkp1);
+        
+            Rupd_func2D_kernel<<<dimGrid,dimBlock>>>(P1, P1_prev, P2, P2_prev, R1, R2, tkp1, tk, multip2, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            if (epsil != 0.0f) {
+                /* calculate norm - stopping rules using the Thrust library */
+                FGPResidCalc2D_kernel<<<dimGrid,dimBlock>>>(d_update, d_update_prev, P1_prev, dimX, dimY, ImSize);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );               
+                
+                thrust::device_vector<float> d_vec(P1_prev, P1_prev + ImSize); 
+                float reduction = sqrt(thrust::transform_reduce(d_vec.begin(), d_vec.end(), square(), 0.0f, thrust::plus<float>()));
+                thrust::device_vector<float> d_vec2(d_update, d_update + ImSize);
+                float reduction2 = sqrt(thrust::transform_reduce(d_vec2.begin(), d_vec2.end(), square(), 0.0f, thrust::plus<float>()));
+                    
+                re = (reduction/reduction2);      
+                if (re < epsil)  count++;
+                    if (count > 4) break;       
+             
+                FGPcopy_kernel2D<<<dimGrid,dimBlock>>>(d_update, d_update_prev, dimX, dimY, ImSize);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );                                              
+            }                  
+        
+            FGPcopy_kernel2D<<<dimGrid,dimBlock>>>(P1, P1_prev, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            FGPcopy_kernel2D<<<dimGrid,dimBlock>>>(P2, P2_prev, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );       
+ 
+            tk = tkp1;
+        }
+        if (printM == 1) printf("FGP-TV iterations stopped at iteration %i \n", i);   
+            /***************************************************************/    
+            //copy result matrix from device to host memory
+            cudaMemcpy(Output,d_update,ImSize*sizeof(float),cudaMemcpyDeviceToHost);
+    
+            cudaFree(d_input);
+            cudaFree(d_update);
+            if (epsil != 0.0f) cudaFree(d_update_prev);
+            cudaFree(P1);
+            cudaFree(P2);
+            cudaFree(P1_prev);
+            cudaFree(P2_prev);
+            cudaFree(R1);
+            cudaFree(R2);
+    }
+    else {
+            /*3D verson*/
+            int ImSize = dimX*dimY*dimZ;    
+            float *d_input, *d_update=NULL, *P1=NULL, *P2=NULL, *P3=NULL, *P1_prev=NULL, *P2_prev=NULL, *P3_prev=NULL, *R1=NULL, *R2=NULL, *R3=NULL;
+   
+            dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+            dim3 dimGrid(idivup(dimX,BLKXSIZE), idivup(dimY,BLKYSIZE),idivup(dimZ,BLKZSIZE));
+    
+            /*allocate space for images on device*/
+            checkCudaErrors( cudaMalloc((void**)&d_input,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&d_update,ImSize*sizeof(float)) );            
+            checkCudaErrors( cudaMalloc((void**)&P1,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P2,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P3,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P1_prev,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P2_prev,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P3_prev,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&R1,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&R2,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&R3,ImSize*sizeof(float)) );
+    
+            checkCudaErrors( cudaMemcpy(d_input,Input,ImSize*sizeof(float),cudaMemcpyHostToDevice));
+            cudaMemset(P1, 0, ImSize*sizeof(float));
+            cudaMemset(P2, 0, ImSize*sizeof(float));
+            cudaMemset(P3, 0, ImSize*sizeof(float));
+            cudaMemset(P1_prev, 0, ImSize*sizeof(float));
+            cudaMemset(P2_prev, 0, ImSize*sizeof(float));
+            cudaMemset(P3_prev, 0, ImSize*sizeof(float));
+            cudaMemset(R1, 0, ImSize*sizeof(float));
+            cudaMemset(R2, 0, ImSize*sizeof(float));
+            cudaMemset(R3, 0, ImSize*sizeof(float));
+            /********************** Run CUDA 3D kernel here ********************/    
+            multip = (1.0f/(26.0f*lambdaPar));
+    
+            /* The main kernel */
+        for (i = 0; i < iter; i++) {
+        
+            /* computing the gradient of the objective function */
+            Obj_func3D_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, R1, R2, R3, dimX, dimY, dimZ, ImSize, lambdaPar);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            if (nonneg != 0) {
+            nonneg3D_kernel<<<dimGrid,dimBlock>>>(d_update, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() ); }
+            
+            /*Taking a step towards minus of the gradient*/
+            Grad_func3D_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, d_update, R1, R2, R3, dimX, dimY, dimZ, ImSize, multip);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            /* projection step */
+            if (methodTV == 0) Proj_func3D_iso_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, dimX, dimY, dimZ, ImSize); /* isotropic kernel */
+            else Proj_func3D_aniso_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, dimX, dimY, dimZ, ImSize); /* anisotropic kernel */
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            multip2 = ((tk-1.0f)/tkp1);
+        
+            Rupd_func3D_kernel<<<dimGrid,dimBlock>>>(P1, P1_prev, P2, P2_prev, P3, P3_prev, R1, R2, R3, tkp1, tk, multip2, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );           
+        
+            FGPcopy_kernel3D<<<dimGrid,dimBlock>>>(P1, P1_prev, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            FGPcopy_kernel3D<<<dimGrid,dimBlock>>>(P2, P2_prev, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );   
+            
+            FGPcopy_kernel3D<<<dimGrid,dimBlock>>>(P3, P3_prev, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );      
+ 
+            tk = tkp1;
+        }
+        if (printM == 1) printf("FGP-TV iterations stopped at iteration %i \n", i);   
+            /***************************************************************/    
+            //copy result matrix from device to host memory
+            cudaMemcpy(Output,d_update,ImSize*sizeof(float),cudaMemcpyDeviceToHost);
+    
+            cudaFree(d_input);
+            cudaFree(d_update);            
+            cudaFree(P1);
+            cudaFree(P2);
+            cudaFree(P3);
+            cudaFree(P1_prev);
+            cudaFree(P2_prev);
+            cudaFree(P3_prev);
+            cudaFree(R1);
+            cudaFree(R2);        
+            cudaFree(R3);        
+    } 
+    //cudaDeviceReset();
+    return 0;
+}
diff --git a/src/Core/regularisers_GPU/TV_FGP_GPU_core.h b/src/Core/regularisers_GPU/TV_FGP_GPU_core.h
new file mode 100755
index 0000000..bf13508
--- /dev/null
+++ b/src/Core/regularisers_GPU/TV_FGP_GPU_core.h
@@ -0,0 +1,9 @@
+#ifndef _TV_FGP_GPU_
+#define _TV_FGP_GPU_
+
+#include "CCPiDefines.h"
+#include <memory.h>
+
+extern "C" CCPI_EXPORT int TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/TV_ROF_GPU_core.cu b/src/Core/regularisers_GPU/TV_ROF_GPU_core.cu
new file mode 100755
index 0000000..76f5be9
--- /dev/null
+++ b/src/Core/regularisers_GPU/TV_ROF_GPU_core.cu
@@ -0,0 +1,358 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "TV_ROF_GPU_core.h"
+
+/* C-OMP implementation of ROF-TV denoising/regularization model [1] (2D/3D case)
+*
+* Input Parameters:
+* 1. Noisy image/volume [REQUIRED]
+* 2. lambda - regularization parameter [REQUIRED]
+* 3. tau - marching step for explicit scheme, ~0.1 is recommended [REQUIRED]
+* 4. Number of iterations, for explicit scheme >= 150 is recommended [REQUIRED]
+*
+* Output:
+* [1] Regularized image/volume
+
+ * This function is based on the paper by
+* [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*
+* D. Kazantsev, 2016-18
+*/
+#include "shared.h"
+    
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+    
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+#define EPS 1.0e-12
+    
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+
+#define MAX(x, y) (((x) > (y)) ? (x) : (y))
+#define MIN(x, y) (((x) < (y)) ? (x) : (y))
+
+__host__ __device__ int sign (float x)
+{
+        return (x > 0) - (x < 0);
+}        
+   
+/*********************2D case****************************/    
+    
+    /* differences 1 */
+    __global__ void D1_func2D(float* Input, float* D1, int N, int M)      
+    {
+		int i1, j1, i2;
+		float NOMx_1,NOMy_1,NOMy_0,denom1,denom2,T1;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;        
+        
+        if ((i >= 0) && (i < N) && (j >= 0) && (j < M)) {
+            
+            /* boundary conditions (Neumann reflections) */
+                i1 = i + 1; if (i1 >= N) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= M) j1 = j-1;
+		
+		     /* Forward-backward differences */
+                NOMx_1 = Input[j1*N + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[j*N + i1] - Input[index]; /* y+ */                
+                NOMy_0 = Input[index] - Input[j*N + i2]; /* y- */
+                
+                denom1 = NOMx_1*NOMx_1;
+                denom2 = 0.5f*(sign((float)NOMy_1) + sign((float)NOMy_0))*(MIN(abs((float)NOMy_1), abs((float)NOMy_0)));
+                denom2 = denom2*denom2;
+                T1 = sqrt(denom1 + denom2 + EPS);
+                D1[index] = NOMx_1/T1;
+		}		
+	}       
+    
+    /* differences 2 */
+    __global__ void D2_func2D(float* Input, float* D2, int N, int M)      
+    {
+		int i1, j1, j2;
+		float NOMx_1,NOMy_1,NOMx_0,denom1,denom2,T2;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;
+        
+        if ((i >= 0) && (i < (N)) && (j >= 0) && (j < (M))) {
+            
+            /* boundary conditions (Neumann reflections) */
+                i1 = i + 1; if (i1 >= N) i1 = i-1;
+                j1 = j + 1; if (j1 >= M) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1; 
+		
+                /* Forward-backward differences */
+                NOMx_1 = Input[j1*N + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[j*N + i1] - Input[index]; /* y+ */
+                NOMx_0 = Input[index] - Input[j2*N + i]; /* x- */
+                
+                denom1 = NOMy_1*NOMy_1;
+                denom2 = 0.5f*(sign((float)NOMx_1) + sign((float)NOMx_0))*(MIN(abs((float)NOMx_1), abs((float)NOMx_0)));
+                denom2 = denom2*denom2;
+                T2 = sqrt(denom1 + denom2 + EPS);
+                D2[index] = NOMy_1/T2;
+		}		
+	}
+    
+    __global__ void TV_kernel2D(float *D1, float *D2, float *Update, float *Input, float lambda, float tau, int N, int M)    
+    {
+		int i2, j2;
+		float dv1,dv2;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        
+        int index = i + N*j;        
+        
+        if ((i >= 0) && (i < (N)) && (j >= 0) && (j < (M))) {
+            
+				/* boundary conditions (Neumann reflections) */
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1; 
+                
+				/* divergence components  */
+                dv1 = D1[index] - D1[j2*N + i];
+                dv2 = D2[index] - D2[j*N + i2];
+                
+                Update[index] += tau*(2.0f*lambda*(dv1 + dv2) - (Update[index] - Input[index]));      
+		
+		}  
+	}   
+/*********************3D case****************************/    
+ 
+    /* differences 1 */
+    __global__ void D1_func3D(float* Input, float* D1, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMy_0, NOMz_1, NOMz_0, denom1, denom2,denom3, T1;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;     
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+            
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = Input[(dimX*dimY)*k + j1*dimX + i] - Input[index]; /* x+ */
+                    NOMy_1 = Input[(dimX*dimY)*k + j*dimX + i1] - Input[index]; /* y+ */                    
+                    NOMy_0 = Input[index] - Input[(dimX*dimY)*k + j*dimX + i2]; /* y- */
+                    
+                    NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+                    NOMz_0 = Input[index] - Input[(dimX*dimY)*k2 + j*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMx_1*NOMx_1;
+                    denom2 = 0.5*(sign(NOMy_1) + sign(NOMy_0))*(MIN(abs(NOMy_1),abs(NOMy_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5*(sign(NOMz_1) + sign(NOMz_0))*(MIN(abs(NOMz_1),abs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T1 = sqrt(denom1 + denom2 + denom3 + EPS);
+                    D1[index] = NOMx_1/T1;	
+		}		
+	}      
+
+    /* differences 2 */
+    __global__ void D2_func3D(float* Input, float* D2, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMx_0, NOMz_1, NOMz_0, denom1, denom2, denom3, T2;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;     
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+                    /* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    
+                    /* Forward-backward differences */
+                    NOMx_1 = Input[(dimX*dimY)*k + (j1)*dimX + i] - Input[index]; /* x+ */
+                    NOMy_1 = Input[(dimX*dimY)*k + (j)*dimX + i1] - Input[index]; /* y+ */
+                    NOMx_0 = Input[index] - Input[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                    NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+                    NOMz_0 = Input[index] - Input[(dimX*dimY)*k2 + (j)*dimX + i]; /* z- */
+                    
+                    
+                    denom1 = NOMy_1*NOMy_1;
+                    denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(abs(NOMx_1),abs(NOMx_0)));
+                    denom2 = denom2*denom2;
+                    denom3 = 0.5*(sign(NOMz_1) + sign(NOMz_0))*(MIN(abs(NOMz_1),abs(NOMz_0)));
+                    denom3 = denom3*denom3;
+                    T2 = sqrt(denom1 + denom2 + denom3 + EPS);
+                    D2[index] = NOMy_1/T2;
+		}
+	}
+	
+	  /* differences 3 */
+    __global__ void D3_func3D(float* Input, float* D3, int dimX, int dimY, int dimZ)      
+    {
+		float NOMx_1, NOMy_1, NOMx_0, NOMy_0, NOMz_1, denom1, denom2, denom3, T3;
+		int i1,i2,k1,j1,j2,k2;
+		
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+      	int index = (dimX*dimY)*k + j*dimX+i;     
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+
+				i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                i2 = i - 1; if (i2 < 0) i2 = i+1;
+                j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                j2 = j - 1; if (j2 < 0) j2 = j+1;
+                k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                k2 = k - 1; if (k2 < 0) k2 = k+1;
+                
+                /* Forward-backward differences */
+                NOMx_1 = Input[(dimX*dimY)*k + (j1)*dimX + i] - Input[index]; /* x+ */
+                NOMy_1 = Input[(dimX*dimY)*k + (j)*dimX + i1] - Input[index]; /* y+ */
+                NOMy_0 = Input[index] - Input[(dimX*dimY)*k + (j)*dimX + i2]; /* y- */
+                NOMx_0 = Input[index] - Input[(dimX*dimY)*k + (j2)*dimX + i]; /* x- */
+                NOMz_1 = Input[(dimX*dimY)*k1 + j*dimX + i] - Input[index]; /* z+ */
+               
+                denom1 = NOMz_1*NOMz_1;
+                denom2 = 0.5*(sign(NOMx_1) + sign(NOMx_0))*(MIN(abs(NOMx_1),abs(NOMx_0)));
+                denom2 = denom2*denom2;
+                denom3 = 0.5*(sign(NOMy_1) + sign(NOMy_0))*(MIN(abs(NOMy_1),abs(NOMy_0)));
+                denom3 = denom3*denom3;
+                T3 = sqrt(denom1 + denom2 + denom3 + EPS);
+                D3[index] = NOMz_1/T3;
+		}
+	}
+
+    __global__ void TV_kernel3D(float *D1, float *D2, float *D3, float *Update, float *Input, float lambda, float tau, int dimX, int dimY, int dimZ)    
+    {
+		float dv1, dv2, dv3;
+		int i1,i2,k1,j1,j2,k2;
+		int i = blockDim.x * blockIdx.x + threadIdx.x;
+        int j = blockDim.y * blockIdx.y + threadIdx.y;
+        int k = blockDim.z * blockIdx.z + threadIdx.z;
+        
+        int index = (dimX*dimY)*k + j*dimX+i;       
+        
+        if ((i >= 0) && (i < dimX) && (j >= 0) && (j < dimY) && (k >= 0) && (k < dimZ)) {
+            
+					/* symmetric boundary conditions (Neuman) */
+                    i1 = i + 1; if (i1 >= dimX) i1 = i-1;
+                    i2 = i - 1; if (i2 < 0) i2 = i+1;
+                    j1 = j + 1; if (j1 >= dimY) j1 = j-1;
+                    j2 = j - 1; if (j2 < 0) j2 = j+1;
+                    k1 = k + 1; if (k1 >= dimZ) k1 = k-1;
+                    k2 = k - 1; if (k2 < 0) k2 = k+1;
+                    
+                    /*divergence components */
+                    dv1 = D1[index] - D1[(dimX*dimY)*k + j2*dimX+i];
+                    dv2 = D2[index] - D2[(dimX*dimY)*k + j*dimX+i2];
+                    dv3 = D3[index] - D3[(dimX*dimY)*k2 + j*dimX+i];
+                    
+                    Update[index] += tau*(2.0f*lambda*(dv1 + dv2 + dv3) - (Update[index] - Input[index]));
+		
+		}  
+	}
+
+/////////////////////////////////////////////////
+// HOST FUNCTION
+extern "C" int TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z)
+{
+	    // set up device
+		int dev = 0;
+		CHECK(cudaSetDevice(dev));
+        float *d_input, *d_update, *d_D1, *d_D2;
+        
+	if (Z == 0) Z = 1;
+        CHECK(cudaMalloc((void**)&d_input,N*M*Z*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_update,N*M*Z*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_D1,N*M*Z*sizeof(float)));
+        CHECK(cudaMalloc((void**)&d_D2,N*M*Z*sizeof(float)));
+        
+        CHECK(cudaMemcpy(d_input,Input,N*M*Z*sizeof(float),cudaMemcpyHostToDevice));
+        CHECK(cudaMemcpy(d_update,Input,N*M*Z*sizeof(float),cudaMemcpyHostToDevice));      
+        
+        if (Z > 1) {
+			// TV - 3D case
+            dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+            dim3 dimGrid(idivup(N,BLKXSIZE), idivup(M,BLKYSIZE),idivup(Z,BLKXSIZE));            
+            
+            float *d_D3;
+            CHECK(cudaMalloc((void**)&d_D3,N*M*Z*sizeof(float)));
+            
+            for(int n=0; n < iter; n++) {
+                /* calculate differences */
+                D1_func3D<<<dimGrid,dimBlock>>>(d_update, d_D1, N, M, Z);
+                CHECK(cudaDeviceSynchronize());
+				D2_func3D<<<dimGrid,dimBlock>>>(d_update, d_D2, N, M, Z);
+                CHECK(cudaDeviceSynchronize());        
+                D3_func3D<<<dimGrid,dimBlock>>>(d_update, d_D3, N, M, Z);
+                CHECK(cudaDeviceSynchronize());        
+                /*running main kernel*/
+                TV_kernel3D<<<dimGrid,dimBlock>>>(d_D1, d_D2, d_D3, d_update, d_input, lambdaPar, tau, N, M, Z);
+                CHECK(cudaDeviceSynchronize());
+            }
+            
+            CHECK(cudaFree(d_D3));
+        }
+        else {
+	    // TV - 2D case
+            dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+            dim3 dimGrid(idivup(N,BLKXSIZE2D), idivup(M,BLKYSIZE2D));
+             
+            for(int n=0; n < iter; n++) {
+                /* calculate differences */
+                D1_func2D<<<dimGrid,dimBlock>>>(d_update, d_D1, N, M);
+                CHECK(cudaDeviceSynchronize());
+				D2_func2D<<<dimGrid,dimBlock>>>(d_update, d_D2, N, M);
+                CHECK(cudaDeviceSynchronize());        
+                /*running main kernel*/
+                TV_kernel2D<<<dimGrid,dimBlock>>>(d_D1, d_D2, d_update, d_input, lambdaPar, tau, N, M);
+                CHECK(cudaDeviceSynchronize());
+            }
+        }        
+        CHECK(cudaMemcpy(Output,d_update,N*M*Z*sizeof(float),cudaMemcpyDeviceToHost));
+        CHECK(cudaFree(d_input));
+        CHECK(cudaFree(d_update));
+        CHECK(cudaFree(d_D1));
+        CHECK(cudaFree(d_D2));        
+        //cudaDeviceReset();
+        return 0;
+}
diff --git a/src/Core/regularisers_GPU/TV_ROF_GPU_core.h b/src/Core/regularisers_GPU/TV_ROF_GPU_core.h
new file mode 100755
index 0000000..3a09296
--- /dev/null
+++ b/src/Core/regularisers_GPU/TV_ROF_GPU_core.h
@@ -0,0 +1,8 @@
+#ifndef __TVGPU_H__
+#define __TVGPU_H__
+#include "CCPiDefines.h"
+#include <stdio.h>
+
+extern "C" CCPI_EXPORT int TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/TV_SB_GPU_core.cu b/src/Core/regularisers_GPU/TV_SB_GPU_core.cu
new file mode 100755
index 0000000..1f494ee
--- /dev/null
+++ b/src/Core/regularisers_GPU/TV_SB_GPU_core.cu
@@ -0,0 +1,552 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+
+#include "TV_SB_GPU_core.h"
+#include "shared.h"
+#include <thrust/device_vector.h>
+#include <thrust/transform_reduce.h>
+
+/* CUDA implementation of Split Bregman - TV denoising-regularisation model (2D/3D) [1]
+*
+* Input Parameters:
+* 1. Noisy image/volume
+* 2. lambda - regularisation parameter
+* 3. Number of iterations [OPTIONAL parameter]
+* 4. eplsilon - tolerance constant [OPTIONAL parameter]
+* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
+* 6. nonneg: 'nonnegativity (0 is OFF by default) [OPTIONAL parameter]
+* 7. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+*
+* Output:
+* 1. Filtered/regularized image
+*
+* [1]. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.
+*/
+
+// This will output the proper CUDA error strings in the event that a CUDA host call returns an error
+
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+struct square { __host__ __device__ float operator()(float x) { return x * x; } };
+
+/************************************************/
+/*****************2D modules*********************/
+/************************************************/
+__global__ void gauss_seidel2D_kernel(float *U, float *A, float *U_prev, float *Dx, float *Dy, float *Bx, float *By, float lambda, float mu, float normConst, int N, int M, int ImSize)
+{
+    
+    float sum;
+    int i1,i2,j1,j2;
+     
+    //calculate each thread global index
+    const int i=blockIdx.x*blockDim.x+threadIdx.x;
+    const int j=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = j*N+i;
+    
+    if ((i < N) && (j < M)) {
+        i1 = i+1; if (i1 == N) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        j1 = j+1; if (j1 == M) j1 = j-1;
+        j2 = j-1; if (j2 < 0) j2 = j+1;
+        
+        sum = Dx[j*N+i2] - Dx[index] + Dy[j2*N+i] - Dy[index] - Bx[j*N+i2] + Bx[index] - By[j2*N+i] + By[index];
+        sum += U_prev[j*N+i1] + U_prev[j*N+i2] + U_prev[j1*N+i] + U_prev[j2*N+i];
+        sum *= lambda;
+        sum += mu*A[index];
+        U[index] = normConst*sum; //Write final result to global memory
+    }
+    return;
+}
+__global__ void updDxDy_shrinkAniso2D_kernel(float *U, float *Dx, float *Dy, float *Bx, float *By, float lambda, int N, int M, int ImSize)
+{
+    
+    int i1,j1;
+    float val1, val11, val2, val22, denom_lam;
+    denom_lam = 1.0f/lambda;
+     
+    //calculate each thread global index
+    const int i=blockIdx.x*blockDim.x+threadIdx.x;
+    const int j=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = j*N+i;
+    
+    if ((i < N) && (j < M)) {
+        i1 = i+1; if (i1 == N) i1 = i-1;
+        j1 = j+1; if (j1 == M) j1 = j-1;
+                
+            val1 = (U[j*N+i1] - U[index]) + Bx[index];
+            val2 = (U[j1*N+i] - U[index]) + By[index];
+            
+            val11 = abs(val1) - denom_lam; if (val11 < 0) val11 = 0;
+            val22 = abs(val2) - denom_lam; if (val22 < 0) val22 = 0;
+            
+            if (val1 !=0) Dx[index] = (val1/abs(val1))*val11; else Dx[index] = 0;
+            if (val2 !=0) Dy[index] = (val2/abs(val2))*val22; else Dy[index] = 0;
+    }
+    return;
+}
+
+__global__ void updDxDy_shrinkIso2D_kernel(float *U, float *Dx, float *Dy, float *Bx, float *By, float lambda, int N, int M, int ImSize)
+{
+    
+    int i1,j1;
+    float val1, val11, val2, denom_lam, denom;
+    denom_lam = 1.0f/lambda;
+     
+    //calculate each thread global index
+    const int i=blockIdx.x*blockDim.x+threadIdx.x;
+    const int j=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = j*N+i;
+    
+    if ((i < N) && (j < M)) {
+        i1 = i+1; if (i1 == N) i1 = i-1;
+        j1 = j+1; if (j1 == M) j1 = j-1;
+        
+            val1 = (U[j*N+i1] - U[index]) + Bx[index];
+            val2 = (U[j1*N+i] - U[index]) + By[index];
+            
+            denom = sqrt(val1*val1 + val2*val2);
+            
+            val11 = (denom - denom_lam); if (val11 < 0) val11 = 0.0f;
+            
+            if (denom != 0.0f) {
+                Dx[index] = val11*(val1/denom);
+                Dy[index] = val11*(val2/denom);
+            }
+            else {
+                Dx[index] = 0;
+                Dy[index] = 0;
+            }
+    }
+    return;
+}
+
+__global__ void updBxBy2D_kernel(float *U, float *Dx, float *Dy, float *Bx, float *By, int N, int M, int ImSize)
+{    
+    int i1,j1;
+     
+    //calculate each thread global index
+    const int i=blockIdx.x*blockDim.x+threadIdx.x;
+    const int j=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = j*N+i;
+    
+    if ((i < N) && (j < M)) {
+            /* symmetric boundary conditions (Neuman) */
+            i1 = i+1; if (i1 == N) i1 = i-1;
+            j1 = j+1; if (j1 == M) j1 = j-1;
+            
+            Bx[index] += (U[j*N+i1] - U[index]) - Dx[index];
+            By[index] += (U[j1*N+i] - U[index]) - Dy[index];
+    }
+    return;
+}
+
+
+/************************************************/
+/*****************3D modules*********************/
+/************************************************/
+__global__ void gauss_seidel3D_kernel(float *U, float *A, float *U_prev, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, float lambda, float mu, float normConst, int N, int M, int Z, int ImSize)
+{
+    
+    float sum,d_val,b_val;
+    int i1,i2,j1,j2,k1,k2;
+     
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {
+        i1 = i+1; if (i1 == N) i1 = i-1;
+        i2 = i-1; if (i2 < 0) i2 = i+1;
+        j1 = j+1; if (j1 == M) j1 = j-1;
+        j2 = j-1; if (j2 < 0) j2 = j+1;
+        k1 = k+1; if (k1 == Z) k1 = k-1;
+        k2 = k-1; if (k2 < 0) k2 = k+1;
+        
+        d_val = Dx[(N*M)*k + j*N+i2] - Dx[index] + Dy[(N*M)*k + j2*N+i] - Dy[index] + Dz[(N*M)*k2 + j*N+i] - Dz[index];
+        b_val = -Bx[(N*M)*k + j*N+i2] + Bx[index] - By[(N*M)*k + j2*N+i] + By[index] - Bz[(N*M)*k2 + j*N+i] + Bz[index];
+        sum = d_val + b_val;
+        sum += U_prev[(N*M)*k + j*N+i1] + U_prev[(N*M)*k + j*N+i2] + U_prev[(N*M)*k + j1*N+i] + U_prev[(N*M)*k + j2*N+i] + U_prev[(N*M)*k1 + j*N+i] + U_prev[(N*M)*k2 + j*N+i];
+        sum *= lambda;
+        sum += mu*A[index];
+        U[index] = normConst*sum;
+    }
+    return;
+}
+__global__ void updDxDy_shrinkAniso3D_kernel(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, float lambda, int N, int M, int Z, int ImSize)
+{
+    
+    int i1,j1,k1;
+    float val1, val11, val2, val3, val22, val33, denom_lam;
+    denom_lam = 1.0f/lambda;
+     
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {
+        i1 = i+1; if (i1 == N) i1 = i-1;
+        j1 = j+1; if (j1 == M) j1 = j-1;
+        k1 = k+1; if (k1 == Z) k1 = k-1;
+                
+            val1 = (U[(N*M)*k + i1 + N*j] - U[index]) + Bx[index];
+            val2 = (U[(N*M)*k + i + N*j1] - U[index]) + By[index];
+            val3 = (U[(N*M)*k1 + i + N*j] - U[index]) + Bz[index];
+            
+            val11 = abs(val1) - denom_lam; if (val11 < 0.0f) val11 = 0.0f;
+            val22 = abs(val2) - denom_lam; if (val22 < 0.0f) val22 = 0.0f;
+            val33 = abs(val3) - denom_lam; if (val33 < 0.0f) val33 = 0.0f;
+            
+            if (val1 !=0.0f) Dx[index] = (val1/abs(val1))*val11; else Dx[index] = 0.0f;
+            if (val2 !=0.0f) Dy[index] = (val2/abs(val2))*val22; else Dy[index] = 0.0f;
+            if (val3 !=0.0f) Dz[index] = (val3/abs(val3))*val33; else Dz[index] = 0.0f;
+    }
+    return;
+}
+
+__global__ void updDxDy_shrinkIso3D_kernel(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, float lambda, int N, int M, int Z, int ImSize)
+{
+    
+    int i1,j1,k1;
+    float val1, val11, val2, val3, denom_lam, denom;
+    denom_lam = 1.0f/lambda;
+     
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {
+        i1 = i+1; if (i1 == N) i1 = i-1;
+        j1 = j+1; if (j1 == M) j1 = j-1;
+        k1 = k+1; if (k1 == Z) k1 = k-1;
+        
+            val1 = (U[(N*M)*k + i1 + N*j] - U[index]) + Bx[index];
+            val2 = (U[(N*M)*k + i + N*j1] - U[index]) + By[index];
+            val3 = (U[(N*M)*k1 + i + N*j] - U[index]) + Bz[index];
+            
+            denom = sqrt(val1*val1 + val2*val2 + val3*val3);
+            
+            val11 = (denom - denom_lam); if (val11 < 0.0f) val11 = 0.0f;
+            
+            if (denom != 0.0f) {
+                Dx[index] = val11*(val1/denom);
+                Dy[index] = val11*(val2/denom);
+                Dz[index] = val11*(val3/denom);
+            }
+            else {
+                Dx[index] = 0.0f;
+                Dy[index] = 0.0f;
+                Dz[index] = 0.0f;
+            }
+    }
+    return;
+}
+
+__global__ void updBxBy3D_kernel(float *U, float *Dx, float *Dy, float *Dz, float *Bx, float *By, float *Bz, int N, int M, int Z, int ImSize)
+{    
+    int i1,j1,k1;
+     
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {
+            /* symmetric boundary conditions (Neuman) */
+            i1 = i+1; if (i1 == N) i1 = i-1;
+            j1 = j+1; if (j1 == M) j1 = j-1;
+            k1 = k+1; if (k1 == Z) k1 = k-1;
+            
+            Bx[index] += (U[(N*M)*k + i1 + N*j] - U[index]) - Dx[index];
+            By[index] += (U[(N*M)*k + i + N*j1] - U[index]) - Dy[index];
+            Bz[index] += (U[(N*M)*k1 + i + N*j] - U[index]) - Dz[index];
+    }
+    return;
+}
+
+__global__ void SBcopy_kernel2D(float *Input, float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        Output[index] = Input[index];
+    }
+}
+
+__global__ void SBcopy_kernel3D(float *Input, float* Output, int N, int M, int Z, int num_total)
+{
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        Output[index] = Input[index];
+    }
+}
+
+__global__ void SBResidCalc2D_kernel(float *Input1, float *Input2, float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        Output[index] = Input1[index] - Input2[index];
+    }
+}
+
+__global__ void SBResidCalc3D_kernel(float *Input1, float *Input2, float* Output, int N, int M, int Z, int num_total)
+{
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        Output[index] = Input1[index] - Input2[index];
+    }
+}
+
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+/********************* MAIN HOST FUNCTION ******************/
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+extern "C" int TV_SB_GPU_main(float *Input, float *Output, float mu, int iter, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ)
+{
+    int deviceCount = -1; // number of devices
+    cudaGetDeviceCount(&deviceCount);
+    if (deviceCount == 0) {
+        fprintf(stderr, "No CUDA devices found\n");
+        return -1;
+    }
+    
+	int ll, DimTotal;
+	float re, lambda, normConst;
+    int count = 0;
+    mu = 1.0f/mu;
+	lambda = 2.0f*mu;
+
+    if (dimZ <= 1) {
+		/*2D verson*/
+		DimTotal = dimX*dimY;
+		normConst = 1.0f/(mu + 4.0f*lambda);
+		float *d_input, *d_update, *d_res, *d_update_prev=NULL, *Dx=NULL, *Dy=NULL, *Bx=NULL, *By=NULL;
+   
+		dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+		dim3 dimGrid(idivup(dimX,BLKXSIZE2D), idivup(dimY,BLKYSIZE2D));
+    
+		/*allocate space for images on device*/
+		checkCudaErrors( cudaMalloc((void**)&d_input,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&d_update,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&d_update_prev,DimTotal*sizeof(float)) );
+		if (epsil != 0.0f) checkCudaErrors( cudaMalloc((void**)&d_res,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Dx,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Dy,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Bx,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&By,DimTotal*sizeof(float)) );
+    
+        checkCudaErrors( cudaMemcpy(d_input,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        checkCudaErrors( cudaMemcpy(d_update,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        cudaMemset(Dx, 0, DimTotal*sizeof(float));
+        cudaMemset(Dy, 0, DimTotal*sizeof(float));
+        cudaMemset(Bx, 0, DimTotal*sizeof(float));
+        cudaMemset(By, 0, DimTotal*sizeof(float));
+
+        /********************** Run CUDA 2D kernels here ********************/   
+        /* The main kernel */
+        for (ll = 0; ll < iter; ll++) {
+        
+        /* storing old value */
+        SBcopy_kernel2D<<<dimGrid,dimBlock>>>(d_update, d_update_prev, dimX, dimY, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() );  
+
+		 /* perform two GS iterations (normally 2 is enough for the convergence) */
+        gauss_seidel2D_kernel<<<dimGrid,dimBlock>>>(d_update, d_input, d_update_prev, Dx, Dy, Bx, By, lambda, mu, normConst, dimX, dimY, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() ); 
+        SBcopy_kernel2D<<<dimGrid,dimBlock>>>(d_update, d_update_prev, dimX, dimY, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() );  
+        /* 2nd GS iteration */
+        gauss_seidel2D_kernel<<<dimGrid,dimBlock>>>(d_update, d_input, d_update_prev, Dx, Dy, Bx, By, lambda, mu, normConst, dimX, dimY, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() ); 
+        
+        /* TV-related step */
+          if (methodTV == 1)  updDxDy_shrinkAniso2D_kernel<<<dimGrid,dimBlock>>>(d_update, Dx, Dy, Bx, By, lambda, dimX, dimY, DimTotal);
+          else updDxDy_shrinkIso2D_kernel<<<dimGrid,dimBlock>>>(d_update, Dx, Dy, Bx, By, lambda, dimX, dimY, DimTotal);
+            
+        /* update for Bregman variables */
+        updBxBy2D_kernel<<<dimGrid,dimBlock>>>(d_update, Dx, Dy, Bx, By, dimX, dimY, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() ); 
+        
+          if (epsil != 0.0f) {
+                /* calculate norm - stopping rules using the Thrust library */
+                SBResidCalc2D_kernel<<<dimGrid,dimBlock>>>(d_update, d_update_prev, d_res, dimX, dimY, DimTotal);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );               
+                
+                thrust::device_vector<float> d_vec(d_res, d_res + DimTotal);
+                float reduction = sqrt(thrust::transform_reduce(d_vec.begin(), d_vec.end(), square(), 0.0f, thrust::plus<float>()));		
+                thrust::device_vector<float> d_vec2(d_update, d_update + DimTotal);  		
+                float reduction2 = sqrt(thrust::transform_reduce(d_vec2.begin(), d_vec2.end(), square(), 0.0f, thrust::plus<float>()));
+                    
+                re = (reduction/reduction2);      
+                if (re < epsil)  count++;
+                    if (count > 4) break;
+          }
+        
+        }
+        if (printM == 1) printf("SB-TV iterations stopped at iteration %i \n", ll);   
+            /***************************************************************/    
+            //copy result matrix from device to host memory
+            cudaMemcpy(Output,d_update,DimTotal*sizeof(float),cudaMemcpyDeviceToHost);
+    
+            cudaFree(d_input);
+            cudaFree(d_update);
+            cudaFree(d_update_prev);
+            if (epsil != 0.0f) cudaFree(d_res);
+            cudaFree(Dx);
+            cudaFree(Dy);
+            cudaFree(Bx);
+            cudaFree(By);
+    }
+    else {
+		/*3D verson*/
+		DimTotal = dimX*dimY*dimZ;
+		normConst = 1.0f/(mu + 6.0f*lambda);
+		float *d_input, *d_update, *d_res, *d_update_prev=NULL, *Dx=NULL, *Dy=NULL, *Dz=NULL, *Bx=NULL, *By=NULL, *Bz=NULL;
+   
+        dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+        dim3 dimGrid(idivup(dimX,BLKXSIZE), idivup(dimY,BLKYSIZE),idivup(dimZ,BLKZSIZE));
+    
+		/*allocate space for images on device*/
+		checkCudaErrors( cudaMalloc((void**)&d_input,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&d_update,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&d_update_prev,DimTotal*sizeof(float)) );
+		if (epsil != 0.0f) checkCudaErrors( cudaMalloc((void**)&d_res,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Dx,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Dy,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Dz,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Bx,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&By,DimTotal*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&Bz,DimTotal*sizeof(float)) );
+    
+        checkCudaErrors( cudaMemcpy(d_input,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        checkCudaErrors( cudaMemcpy(d_update,Input,DimTotal*sizeof(float),cudaMemcpyHostToDevice));
+        cudaMemset(Dx, 0, DimTotal*sizeof(float));
+        cudaMemset(Dy, 0, DimTotal*sizeof(float));
+        cudaMemset(Dz, 0, DimTotal*sizeof(float));
+        cudaMemset(Bx, 0, DimTotal*sizeof(float));
+        cudaMemset(By, 0, DimTotal*sizeof(float));
+        cudaMemset(Bz, 0, DimTotal*sizeof(float));
+
+        /********************** Run CUDA 3D kernels here ********************/   
+        /* The main kernel */
+        for (ll = 0; ll < iter; ll++) {
+        
+        /* storing old value */
+        SBcopy_kernel3D<<<dimGrid,dimBlock>>>(d_update, d_update_prev, dimX, dimY, dimZ, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() );
+
+		 /* perform two GS iterations (normally 2 is enough for the convergence) */
+        gauss_seidel3D_kernel<<<dimGrid,dimBlock>>>(d_update, d_input, d_update_prev, Dx, Dy, Dz, Bx, By, Bz, lambda, mu, normConst, dimX, dimY, dimZ, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() ); 
+        SBcopy_kernel3D<<<dimGrid,dimBlock>>>(d_update, d_update_prev, dimX, dimY, dimZ, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() );  
+        /* 2nd GS iteration */
+        gauss_seidel3D_kernel<<<dimGrid,dimBlock>>>(d_update, d_input, d_update_prev, Dx, Dy, Dz, Bx, By, Bz, lambda, mu, normConst, dimX, dimY, dimZ, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() ); 
+        
+        /* TV-related step */
+          if (methodTV == 1)  updDxDy_shrinkAniso3D_kernel<<<dimGrid,dimBlock>>>(d_update, Dx, Dy, Dz, Bx, By, Bz, lambda, dimX, dimY, dimZ, DimTotal);
+          else updDxDy_shrinkIso3D_kernel<<<dimGrid,dimBlock>>>(d_update, Dx, Dy, Dz, Bx, By, Bz, lambda, dimX, dimY, dimZ, DimTotal);
+            
+        /* update for Bregman variables */
+        updBxBy3D_kernel<<<dimGrid,dimBlock>>>(d_update, Dx, Dy, Dz, Bx, By, Bz, dimX, dimY, dimZ, DimTotal);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() ); 
+        
+          if (epsil != 0.0f) {
+                /* calculate norm - stopping rules using the Thrust library */
+                SBResidCalc3D_kernel<<<dimGrid,dimBlock>>>(d_update, d_update_prev, d_res, dimX, dimY, dimZ, DimTotal);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );               
+                
+                thrust::device_vector<float> d_vec(d_res, d_res + DimTotal);
+                float reduction = sqrt(thrust::transform_reduce(d_vec.begin(), d_vec.end(), square(), 0.0f, thrust::plus<float>()));		
+                thrust::device_vector<float> d_vec2(d_update, d_update + DimTotal);  		
+                float reduction2 = sqrt(thrust::transform_reduce(d_vec2.begin(), d_vec2.end(), square(), 0.0f, thrust::plus<float>()));
+                    
+                re = (reduction/reduction2);
+                if (re < epsil)  count++;
+                    if (count > 4) break;
+          }
+        }
+        if (printM == 1) printf("SB-TV iterations stopped at iteration %i \n", ll);   
+            /***************************************************************/    
+            //copy result matrix from device to host memory
+            cudaMemcpy(Output,d_update,DimTotal*sizeof(float),cudaMemcpyDeviceToHost);
+    
+            cudaFree(d_input);
+            cudaFree(d_update);
+            cudaFree(d_update_prev);
+            if (epsil != 0.0f) cudaFree(d_res);
+            cudaFree(Dx);
+            cudaFree(Dy);
+            cudaFree(Dz);
+            cudaFree(Bx);
+            cudaFree(By);
+            cudaFree(Bz);
+    } 
+    //cudaDeviceReset();
+    return 0;
+}
diff --git a/src/Core/regularisers_GPU/TV_SB_GPU_core.h b/src/Core/regularisers_GPU/TV_SB_GPU_core.h
new file mode 100755
index 0000000..901b90f
--- /dev/null
+++ b/src/Core/regularisers_GPU/TV_SB_GPU_core.h
@@ -0,0 +1,10 @@
+#ifndef _SB_TV_GPU_
+#define _SB_TV_GPU_
+
+#include "CCPiDefines.h"
+#include <memory.h>
+
+
+extern "C" CCPI_EXPORT int TV_SB_GPU_main(float *Input, float *Output, float mu, int iter, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/dTV_FGP_GPU_core.cu b/src/Core/regularisers_GPU/dTV_FGP_GPU_core.cu
new file mode 100644
index 0000000..7503ec7
--- /dev/null
+++ b/src/Core/regularisers_GPU/dTV_FGP_GPU_core.cu
@@ -0,0 +1,741 @@
+ /*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/ 
+#include "shared.h"
+#include "dTV_FGP_GPU_core.h"
+#include <thrust/device_vector.h>
+#include <thrust/transform_reduce.h>
+
+/* CUDA implementation of FGP-dTV [1,2] denoising/regularization model (2D/3D case)
+ * which employs structural similarity of the level sets of two images/volumes, see [1,2]
+ * The current implementation updates image 1 while image 2 is being fixed.
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. Additional reference image/volume of the same dimensions as (1) [REQUIRED]
+ * 3. lambdaPar - regularization parameter [REQUIRED]
+ * 4. Number of iterations [OPTIONAL]
+ * 5. eplsilon: tolerance constant [OPTIONAL]
+ * 6. eta: smoothing constant to calculate gradient of the reference [OPTIONAL] * 
+ * 7. TV-type: methodTV - 'iso' (0) or 'l1' (1) [OPTIONAL]
+ * 8. nonneg: 'nonnegativity (0 is OFF by default) [OPTIONAL]
+ * 9. print information: 0 (off) or 1 (on) [OPTIONAL]
+ *
+ * Output:
+ * [1] Filtered/regularized image/volume
+ *
+ * This function is based on the Matlab's codes and papers by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ * [2] M. J. Ehrhardt and M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), pp. 1084–1106
+ */
+ 
+
+#define BLKXSIZE2D 16
+#define BLKYSIZE2D 16
+
+#define BLKXSIZE 8
+#define BLKYSIZE 8
+#define BLKZSIZE 8
+
+#define idivup(a, b) ( ((a)%(b) != 0) ? (a)/(b)+1 : (a)/(b) )
+struct square { __host__ __device__ float operator()(float x) { return x * x; } };
+
+/************************************************/
+/*****************2D modules*********************/
+/************************************************/
+
+__global__ void GradNorm_func2D_kernel(float *Refd, float *Refd_x, float *Refd_y, float eta, int N, int M, int ImSize)
+{
+    
+    float val1, val2, gradX, gradY, magn;
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex; 
+    
+    if ((xIndex < N) && (yIndex < M)) {        
+        /* boundary conditions */
+        if (xIndex >= N-1) val1 = 0.0f; else val1 =  Refd[(xIndex+1) + N*yIndex];
+        if (yIndex >= M-1) val2 = 0.0f; else val2 =  Refd[(xIndex) + N*(yIndex + 1)];        
+        
+            gradX = val1 - Refd[index];
+            gradY = val2 - Refd[index];
+            magn = pow(gradX,2) + pow(gradY,2);
+            magn = sqrt(magn + pow(eta,2));
+            Refd_x[index] = gradX/magn;
+            Refd_y[index] = gradY/magn;         
+    }
+    return;
+}
+
+__global__ void ProjectVect_func2D_kernel(float *R1, float *R2, float *Refd_x, float *Refd_y, int N, int M, int ImSize)
+{
+    
+    float in_prod;
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex; 
+    
+    if ((xIndex < N) && (yIndex < M)) {
+        in_prod = R1[index]*Refd_x[index] + R2[index]*Refd_y[index];   /* calculate inner product */
+        R1[index] = R1[index] - in_prod*Refd_x[index];
+        R2[index] = R2[index] - in_prod*Refd_y[index];       
+    }
+    return;
+}
+
+
+__global__ void Obj_dfunc2D_kernel(float *Ad, float *D, float *R1, float *R2, int N, int M, int ImSize, float lambda)
+{
+    
+    float val1,val2;
+    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex; 
+    
+    if ((xIndex < N) && (yIndex < M)) {        
+        if (xIndex <= 0) {val1 = 0.0f;} else {val1 = R1[(xIndex-1) + N*yIndex];}
+        if (yIndex <= 0) {val2 = 0.0f;} else {val2 = R2[xIndex + N*(yIndex-1)];}
+        
+        //Write final result to global memory
+        D[index] = Ad[index] - lambda*(R1[index] + R2[index] - val1 - val2);
+    }
+    return;
+}
+
+__global__ void Grad_dfunc2D_kernel(float *P1, float *P2, float *D, float *R1, float *R2,  float *Refd_x, float *Refd_y, int N, int M, int ImSize, float multip)
+{
+    
+    float val1,val2,in_prod;
+    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) {        
+        
+        /* boundary conditions */
+        if (xIndex >= N-1) val1 = 0.0f; else val1 = D[index] - D[(xIndex+1) + N*yIndex];
+        if (yIndex >= M-1) val2 = 0.0f; else val2 = D[index] - D[(xIndex) + N*(yIndex + 1)];
+        
+        in_prod = val1*Refd_x[index] + val2*Refd_y[index];   /* calculate inner product */
+        val1 = val1 - in_prod*Refd_x[index];
+        val2 = val2 - in_prod*Refd_y[index];   
+        
+        //Write final result to global memory
+        P1[index] = R1[index] + multip*val1;
+        P2[index] = R2[index] + multip*val2;
+    }
+    return;
+}
+
+__global__ void Proj_dfunc2D_iso_kernel(float *P1, float *P2, int N, int M, int ImSize)
+{
+    
+    float denom;    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) { 
+        denom = pow(P1[index],2) +  pow(P2[index],2);        
+        if (denom > 1.0f) {
+            P1[index] = P1[index]/sqrt(denom);
+            P2[index] = P2[index]/sqrt(denom);
+        }
+    }
+    return;
+}
+__global__ void Proj_dfunc2D_aniso_kernel(float *P1, float *P2, int N, int M, int ImSize)
+{
+    
+    float val1, val2;    
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) { 
+                val1 = abs(P1[index]);
+                val2 = abs(P2[index]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                P1[index] = P1[index]/val1;
+                P2[index] = P2[index]/val2;
+    }
+    return;
+}
+__global__ void Rupd_dfunc2D_kernel(float *P1, float *P1_old, float *P2, float *P2_old, float *R1, float *R2, float tkp1, float tk, float multip2, int N, int M, int ImSize)
+{
+    //calculate each thread global index
+    const int xIndex=blockIdx.x*blockDim.x+threadIdx.x;
+    const int yIndex=blockIdx.y*blockDim.y+threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if ((xIndex < N) && (yIndex < M)) { 
+        R1[index] = P1[index] + multip2*(P1[index] - P1_old[index]);
+        R2[index] = P2[index] + multip2*(P2[index] - P2_old[index]);
+    }
+    return;
+}
+__global__ void dTVnonneg2D_kernel(float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        if (Output[index] < 0.0f) Output[index] = 0.0f;
+    }
+}
+__global__ void dTVcopy_kernel2D(float *Input, float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        Output[index] = Input[index];
+    }
+}
+
+__global__ void dTVcopy_kernel3D(float *Input, float* Output, int N, int M, int Z, int num_total)
+{
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        Output[index] = Input[index];
+    }
+}
+
+__global__ void dTVResidCalc2D_kernel(float *Input1, float *Input2, float* Output, int N, int M, int num_total)
+{
+    int xIndex = blockDim.x * blockIdx.x + threadIdx.x;
+    int yIndex = blockDim.y * blockIdx.y + threadIdx.y;
+    
+    int index = xIndex + N*yIndex;
+    
+    if (index < num_total)	{
+        Output[index] = Input1[index] - Input2[index];
+    }
+}
+
+__global__ void dTVResidCalc3D_kernel(float *Input1, float *Input2, float* Output, int N, int M, int Z, int num_total)
+{
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        Output[index] = Input1[index] - Input2[index];
+    }
+}
+
+/************************************************/
+/*****************3D modules*********************/
+/************************************************/
+__global__ void GradNorm_func3D_kernel(float *Refd, float *Refd_x, float *Refd_y, float *Refd_z, float eta, int N, int M, int Z, int ImSize)
+{
+    
+    float val1, val2, val3, gradX, gradY, gradZ, magn;
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {  
+        /* boundary conditions */
+        if (i >= N-1) val1 = 0.0f; else val1 =  Refd[(N*M)*k + (i+1) + N*j];
+        if (j >= M-1) val2 = 0.0f; else val2 =  Refd[(N*M)*k + i + N*(j+1)];
+        if (k >= Z-1) val3 = 0.0f; else val3 =  Refd[(N*M)*(k+1) + i + N*j];
+        
+            gradX = val1 - Refd[index];
+            gradY = val2 - Refd[index];
+            gradZ = val3 - Refd[index];
+            magn = pow(gradX,2) + pow(gradY,2) + pow(gradZ,2);
+            magn = sqrt(magn + pow(eta,2));
+            Refd_x[index] = gradX/magn;
+            Refd_y[index] = gradY/magn;
+            Refd_z[index] = gradZ/magn;
+    }
+    return;
+}
+
+__global__ void ProjectVect_func3D_kernel(float *R1, float *R2, float *R3, float *Refd_x, float *Refd_y, float *Refd_z, int N, int M, int Z, int ImSize)
+{
+    
+    float in_prod;
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {
+        in_prod = R1[index]*Refd_x[index] + R2[index]*Refd_y[index] + R3[index]*Refd_z[index]; /* calculate inner product */
+        
+        R1[index] = R1[index] - in_prod*Refd_x[index];
+        R2[index] = R2[index] - in_prod*Refd_y[index];
+        R3[index] = R3[index] - in_prod*Refd_z[index];
+    }
+    return;
+}
+
+
+__global__ void Obj_dfunc3D_kernel(float *Ad, float *D, float *R1, float *R2, float *R3, int N, int M, int Z, int ImSize, float lambda)
+{
+    
+    float val1,val2,val3;
+    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k < Z)) {
+        if (i <= 0) {val1 = 0.0f;} else {val1 = R1[(N*M)*(k) + (i-1) + N*j];}
+        if (j <= 0) {val2 = 0.0f;} else {val2 = R2[(N*M)*(k) + i + N*(j-1)];}
+        if (k <= 0) {val3 = 0.0f;} else {val3 = R3[(N*M)*(k-1) + i + N*j];}
+        //Write final result to global memory
+        D[index] = Ad[index] - lambda*(R1[index] + R2[index] + R3[index] - val1 - val2 - val3);
+    }
+    return;
+}
+
+__global__ void Grad_dfunc3D_kernel(float *P1, float *P2, float *P3, float *D, float *R1, float *R2, float *R3, float *Refd_x, float *Refd_y, float *Refd_z, int N, int M, int Z, int ImSize, float multip)
+{
+    
+    float val1,val2,val3,in_prod;
+    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) {
+        /* boundary conditions */
+        if (i >= N-1) val1 = 0.0f; else val1 = D[index] - D[(N*M)*(k) + (i+1) + N*j];
+        if (j >= M-1) val2 = 0.0f; else val2 = D[index] - D[(N*M)*(k) + i + N*(j+1)];
+        if (k >= Z-1) val3 = 0.0f; else val3 = D[index] - D[(N*M)*(k+1) + i + N*j];       
+        
+        in_prod = val1*Refd_x[index] + val2*Refd_y[index] + val3*Refd_z[index];   /* calculate inner product */
+        val1 = val1 - in_prod*Refd_x[index];
+        val2 = val2 - in_prod*Refd_y[index];
+        val3 = val3 - in_prod*Refd_z[index];
+        
+        //Write final result to global memory
+        P1[index] = R1[index] + multip*val1;
+        P2[index] = R2[index] + multip*val2;
+        P3[index] = R3[index] + multip*val3;
+    }
+    return;
+}
+
+__global__ void Proj_dfunc3D_iso_kernel(float *P1, float *P2, float *P3, int N, int M, int Z, int ImSize)
+{
+    
+    float denom,sq_denom;    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) {
+        denom = pow(P1[index],2) +  pow(P2[index],2) + pow(P3[index],2);
+        
+        if (denom > 1.0f) {
+            sq_denom = 1.0f/sqrt(denom);
+            P1[index] = P1[index]*sq_denom;
+            P2[index] = P2[index]*sq_denom;
+            P3[index] = P3[index]*sq_denom;
+        }
+    }
+    return;
+}
+
+__global__ void Proj_dfunc3D_aniso_kernel(float *P1, float *P2, float *P3, int N, int M, int Z, int ImSize)
+{
+    
+    float val1, val2, val3;    
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) {
+                val1 = abs(P1[index]);
+                val2 = abs(P2[index]);
+                val3 = abs(P3[index]);
+                if (val1 < 1.0f) {val1 = 1.0f;}
+                if (val2 < 1.0f) {val2 = 1.0f;}
+                if (val3 < 1.0f) {val3 = 1.0f;}
+                P1[index] = P1[index]/val1;
+                P2[index] = P2[index]/val2;
+                P3[index] = P3[index]/val3;
+    }
+    return;
+}
+
+
+__global__ void Rupd_dfunc3D_kernel(float *P1, float *P1_old, float *P2, float *P2_old, float *P3, float *P3_old, float *R1, float *R2, float *R3, float tkp1, float tk, float multip2, int N, int M, int Z, int ImSize)
+{
+    //calculate each thread global index
+	int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if ((i < N) && (j < M) && (k <  Z)) { 
+        R1[index] = P1[index] + multip2*(P1[index] - P1_old[index]);
+        R2[index] = P2[index] + multip2*(P2[index] - P2_old[index]);
+        R3[index] = P3[index] + multip2*(P3[index] - P3_old[index]);
+    }
+    return;
+}
+
+__global__ void dTVnonneg3D_kernel(float* Output, int N, int M, int Z, int num_total)
+{
+    int i = blockDim.x * blockIdx.x + threadIdx.x;
+    int j = blockDim.y * blockIdx.y + threadIdx.y;
+    int k = blockDim.z * blockIdx.z + threadIdx.z;
+    
+    int index = (N*M)*k + i + N*j;
+    
+    if (index < num_total)	{
+        if (Output[index] < 0.0f) Output[index] = 0.0f;
+    }
+}
+/*%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%*/
+
+////////////MAIN HOST FUNCTION ///////////////
+extern "C" int dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iter, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ)
+{
+    int deviceCount = -1; // number of devices
+    cudaGetDeviceCount(&deviceCount);
+    if (deviceCount == 0) {
+        fprintf(stderr, "No CUDA devices found\n");
+        return -1;
+    }
+    
+    int count = 0, i;
+    float re, multip,multip2;    
+	float tk = 1.0f;
+    float tkp1=1.0f;
+        
+    if (dimZ <= 1) {
+		/*2D verson*/
+		int ImSize = dimX*dimY;    
+		float *d_input, *d_update=NULL, *d_update_prev=NULL, *P1=NULL, *P2=NULL, *P1_prev=NULL, *P2_prev=NULL, *R1=NULL, *R2=NULL, *InputRef_x=NULL, *InputRef_y=NULL, *d_InputRef=NULL;
+   
+		dim3 dimBlock(BLKXSIZE2D,BLKYSIZE2D);
+		dim3 dimGrid(idivup(dimX,BLKXSIZE2D), idivup(dimY,BLKYSIZE2D));
+    
+		/*allocate space for images on device*/
+		checkCudaErrors( cudaMalloc((void**)&d_input,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&d_update,ImSize*sizeof(float)) );
+		if (epsil != 0.0f) checkCudaErrors( cudaMalloc((void**)&d_update_prev,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P1,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P2,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P1_prev,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&P2_prev,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&R1,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&R2,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&d_InputRef,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&InputRef_x,ImSize*sizeof(float)) );
+		checkCudaErrors( cudaMalloc((void**)&InputRef_y,ImSize*sizeof(float)) );
+    
+        checkCudaErrors( cudaMemcpy(d_input,Input,ImSize*sizeof(float),cudaMemcpyHostToDevice));
+        checkCudaErrors( cudaMemcpy(d_InputRef,InputRef,ImSize*sizeof(float),cudaMemcpyHostToDevice));
+        
+        cudaMemset(P1, 0, ImSize*sizeof(float));
+        cudaMemset(P2, 0, ImSize*sizeof(float));
+        cudaMemset(P1_prev, 0, ImSize*sizeof(float));
+        cudaMemset(P2_prev, 0, ImSize*sizeof(float));
+        cudaMemset(R1, 0, ImSize*sizeof(float));
+        cudaMemset(R2, 0, ImSize*sizeof(float));
+        cudaMemset(InputRef_x, 0, ImSize*sizeof(float));
+        cudaMemset(InputRef_y, 0, ImSize*sizeof(float));
+        
+        /******************** Run CUDA 2D kernel here ********************/
+        multip = (1.0f/(8.0f*lambdaPar));
+        /* calculate gradient vectors for the reference */
+        GradNorm_func2D_kernel<<<dimGrid,dimBlock>>>(d_InputRef, InputRef_x, InputRef_y, eta, dimX, dimY, ImSize);
+        checkCudaErrors( cudaDeviceSynchronize() );
+        checkCudaErrors(cudaPeekAtLastError() );
+    
+        /* The main kernel */
+        for (i = 0; i < iter; i++) {
+        
+            /*projects a 2D vector field R-1,2 onto the orthogonal complement of another 2D vector field InputRef_xy*/         
+            ProjectVect_func2D_kernel<<<dimGrid,dimBlock>>>(R1, R2, InputRef_x, InputRef_y, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+            
+            /* computing the gradient of the objective function */
+            Obj_dfunc2D_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, R1, R2, dimX, dimY, ImSize, lambdaPar);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+            
+            if (nonneg != 0) {
+            dTVnonneg2D_kernel<<<dimGrid,dimBlock>>>(d_update, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() ); }
+                    
+            /*Taking a step towards minus of the gradient*/
+            Grad_dfunc2D_kernel<<<dimGrid,dimBlock>>>(P1, P2, d_update, R1, R2, InputRef_x, InputRef_y, dimX, dimY, ImSize, multip);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            /* projection step */
+            if (methodTV == 0) Proj_dfunc2D_iso_kernel<<<dimGrid,dimBlock>>>(P1, P2, dimX, dimY, ImSize); /*isotropic TV*/
+            else Proj_dfunc2D_aniso_kernel<<<dimGrid,dimBlock>>>(P1, P2, dimX, dimY, ImSize); /*anisotropic TV*/            
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            multip2 = ((tk-1.0f)/tkp1);
+        
+            Rupd_dfunc2D_kernel<<<dimGrid,dimBlock>>>(P1, P1_prev, P2, P2_prev, R1, R2, tkp1, tk, multip2, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            if (epsil != 0.0f) {
+                /* calculate norm - stopping rules using the Thrust library */
+                dTVResidCalc2D_kernel<<<dimGrid,dimBlock>>>(d_update, d_update_prev, P1_prev, dimX, dimY, ImSize);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );               
+                
+                thrust::device_vector<float> d_vec(P1_prev, P1_prev + ImSize); 
+                float reduction = sqrt(thrust::transform_reduce(d_vec.begin(), d_vec.end(), square(), 0.0f, thrust::plus<float>()));
+                thrust::device_vector<float> d_vec2(d_update, d_update + ImSize);
+                float reduction2 = sqrt(thrust::transform_reduce(d_vec2.begin(), d_vec2.end(), square(), 0.0f, thrust::plus<float>()));
+                    
+                re = (reduction/reduction2);      
+                if (re < epsil)  count++;
+                    if (count > 4) break;       
+             
+                dTVcopy_kernel2D<<<dimGrid,dimBlock>>>(d_update, d_update_prev, dimX, dimY, ImSize);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );                                              
+            }
+        
+            dTVcopy_kernel2D<<<dimGrid,dimBlock>>>(P1, P1_prev, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            dTVcopy_kernel2D<<<dimGrid,dimBlock>>>(P2, P2_prev, dimX, dimY, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );       
+ 
+            tk = tkp1;
+        }
+        if (printM == 1) printf("FGP-dTV iterations stopped at iteration %i \n", i);   
+            /***************************************************************/    
+            //copy result matrix from device to host memory
+            cudaMemcpy(Output,d_update,ImSize*sizeof(float),cudaMemcpyDeviceToHost);
+    
+            cudaFree(d_input);
+            cudaFree(d_update);
+            if (epsil != 0.0f) cudaFree(d_update_prev);
+            cudaFree(P1);
+            cudaFree(P2);
+            cudaFree(P1_prev);
+            cudaFree(P2_prev);
+            cudaFree(R1);
+            cudaFree(R2);
+            
+            cudaFree(d_InputRef);
+            cudaFree(InputRef_x);
+            cudaFree(InputRef_y);
+    }
+    else {
+            /*3D verson*/
+            int ImSize = dimX*dimY*dimZ;    
+            float *d_input, *d_update=NULL, *d_update_prev, *P1=NULL, *P2=NULL, *P3=NULL, *P1_prev=NULL, *P2_prev=NULL, *P3_prev=NULL, *R1=NULL, *R2=NULL, *R3=NULL, *InputRef_x=NULL, *InputRef_y=NULL, *InputRef_z=NULL, *d_InputRef=NULL;
+   
+            dim3 dimBlock(BLKXSIZE,BLKYSIZE,BLKZSIZE);
+            dim3 dimGrid(idivup(dimX,BLKXSIZE), idivup(dimY,BLKYSIZE),idivup(dimZ,BLKZSIZE));
+    
+            /*allocate space for images on device*/
+            checkCudaErrors( cudaMalloc((void**)&d_input,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&d_update,ImSize*sizeof(float)) );
+            if (epsil != 0.0f) checkCudaErrors( cudaMalloc((void**)&d_update_prev,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P1,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P2,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P3,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P1_prev,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P2_prev,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&P3_prev,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&R1,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&R2,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&R3,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&d_InputRef,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&InputRef_x,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&InputRef_y,ImSize*sizeof(float)) );
+            checkCudaErrors( cudaMalloc((void**)&InputRef_z,ImSize*sizeof(float)) );    
+    
+            checkCudaErrors( cudaMemcpy(d_input,Input,ImSize*sizeof(float),cudaMemcpyHostToDevice));
+            checkCudaErrors( cudaMemcpy(d_InputRef,InputRef,ImSize*sizeof(float),cudaMemcpyHostToDevice));
+            
+            cudaMemset(P1, 0, ImSize*sizeof(float));
+            cudaMemset(P2, 0, ImSize*sizeof(float));
+            cudaMemset(P3, 0, ImSize*sizeof(float));
+            cudaMemset(P1_prev, 0, ImSize*sizeof(float));
+            cudaMemset(P2_prev, 0, ImSize*sizeof(float));
+            cudaMemset(P3_prev, 0, ImSize*sizeof(float));
+            cudaMemset(R1, 0, ImSize*sizeof(float));
+            cudaMemset(R2, 0, ImSize*sizeof(float));
+            cudaMemset(R3, 0, ImSize*sizeof(float));
+            cudaMemset(InputRef_x, 0, ImSize*sizeof(float));
+            cudaMemset(InputRef_y, 0, ImSize*sizeof(float));
+            cudaMemset(InputRef_z, 0, ImSize*sizeof(float));
+            
+            /********************** Run CUDA 3D kernel here ********************/    
+            multip = (1.0f/(26.0f*lambdaPar));
+            /* calculate gradient vectors for the reference */
+            GradNorm_func3D_kernel<<<dimGrid,dimBlock>>>(d_InputRef, InputRef_x, InputRef_y, InputRef_z, eta, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+    
+            /* The main kernel */
+        for (i = 0; i < iter; i++) {
+
+			/*projects a 3D vector field R-1,2,3 onto the orthogonal complement of another 3D vector field InputRef_xyz*/
+            ProjectVect_func3D_kernel<<<dimGrid,dimBlock>>>(R1, R2, R3, InputRef_x, InputRef_y, InputRef_z, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            /* computing the gradient of the objective function */
+            Obj_dfunc3D_kernel<<<dimGrid,dimBlock>>>(d_input, d_update, R1, R2, R3, dimX, dimY, dimZ, ImSize, lambdaPar);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            if (nonneg != 0) {
+            dTVnonneg3D_kernel<<<dimGrid,dimBlock>>>(d_update, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() ); }
+            
+            /*Taking a step towards minus of the gradient*/
+            Grad_dfunc3D_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, d_update, R1, R2, R3, InputRef_x, InputRef_y, InputRef_z, dimX, dimY, dimZ, ImSize, multip);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            /* projection step */
+            if (methodTV == 0) Proj_dfunc3D_iso_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, dimX, dimY, dimZ, ImSize); /* isotropic kernel */
+            else Proj_dfunc3D_aniso_kernel<<<dimGrid,dimBlock>>>(P1, P2, P3, dimX, dimY, dimZ, ImSize); /* anisotropic kernel */
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            tkp1 = (1.0f + sqrt(1.0f + 4.0f*tk*tk))*0.5f;
+            multip2 = ((tk-1.0f)/tkp1);
+        
+            Rupd_dfunc3D_kernel<<<dimGrid,dimBlock>>>(P1, P1_prev, P2, P2_prev, P3, P3_prev, R1, R2, R3, tkp1, tk, multip2, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+            
+            if (epsil != 0.0f) {
+                /* calculate norm - stopping rules using the Thrust library */
+                dTVResidCalc3D_kernel<<<dimGrid,dimBlock>>>(d_update, d_update_prev, P1_prev, dimX, dimY, dimZ, ImSize);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );               
+                
+                thrust::device_vector<float> d_vec(P1_prev, P1_prev + ImSize); 
+                float reduction = sqrt(thrust::transform_reduce(d_vec.begin(), d_vec.end(), square(), 0.0f, thrust::plus<float>()));
+                thrust::device_vector<float> d_vec2(d_update, d_update + ImSize);
+                float reduction2 = sqrt(thrust::transform_reduce(d_vec2.begin(), d_vec2.end(), square(), 0.0f, thrust::plus<float>()));
+                    
+                re = (reduction/reduction2);      
+                if (re < epsil)  count++;
+                    if (count > 4) break;       
+             
+                dTVcopy_kernel3D<<<dimGrid,dimBlock>>>(d_update, d_update_prev, dimX, dimY, dimZ, ImSize);
+                checkCudaErrors( cudaDeviceSynchronize() );
+                checkCudaErrors(cudaPeekAtLastError() );
+            }
+        
+            dTVcopy_kernel3D<<<dimGrid,dimBlock>>>(P1, P1_prev, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );
+        
+            dTVcopy_kernel3D<<<dimGrid,dimBlock>>>(P2, P2_prev, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );   
+            
+            dTVcopy_kernel3D<<<dimGrid,dimBlock>>>(P3, P3_prev, dimX, dimY, dimZ, ImSize);
+            checkCudaErrors( cudaDeviceSynchronize() );
+            checkCudaErrors(cudaPeekAtLastError() );      
+ 
+            tk = tkp1;
+        }
+        if (printM == 1) printf("FGP-dTV iterations stopped at iteration %i \n", i);   
+            /***************************************************************/    
+            //copy result matrix from device to host memory
+            cudaMemcpy(Output,d_update,ImSize*sizeof(float),cudaMemcpyDeviceToHost);
+    
+            cudaFree(d_input);
+            cudaFree(d_update);
+            if (epsil != 0.0f) cudaFree(d_update_prev);
+            cudaFree(P1);
+            cudaFree(P2);
+            cudaFree(P3);
+            cudaFree(P1_prev);
+            cudaFree(P2_prev);
+            cudaFree(P3_prev);
+            cudaFree(R1);
+            cudaFree(R2);
+            cudaFree(R3);
+            cudaFree(InputRef_x);
+            cudaFree(InputRef_y);
+            cudaFree(InputRef_z);
+            cudaFree(d_InputRef);
+    }
+    //cudaDeviceReset();
+    return 0;
+}
diff --git a/src/Core/regularisers_GPU/dTV_FGP_GPU_core.h b/src/Core/regularisers_GPU/dTV_FGP_GPU_core.h
new file mode 100644
index 0000000..f9281e8
--- /dev/null
+++ b/src/Core/regularisers_GPU/dTV_FGP_GPU_core.h
@@ -0,0 +1,9 @@
+#ifndef _dTV_FGP_GPU_
+#define _dTV_FGP_GPU_
+
+#include "CCPiDefines.h"
+#include <memory.h>
+
+extern "C" CCPI_EXPORT int dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iter, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+
+#endif 
diff --git a/src/Core/regularisers_GPU/shared.h b/src/Core/regularisers_GPU/shared.h
new file mode 100644
index 0000000..fe98cd6
--- /dev/null
+++ b/src/Core/regularisers_GPU/shared.h
@@ -0,0 +1,42 @@
+/*shared macros*/
+
+
+/*checks CUDA call, should be used in functions returning <int> value
+if error happens, writes to standard error and explicitly returns -1*/
+#define CHECK(call)                                                            \
+{                                                                              \
+    const cudaError_t error = call;                                            \
+    if (error != cudaSuccess)                                                  \
+    {                                                                          \
+        fprintf(stderr, "Error: %s:%d, ", __FILE__, __LINE__);                 \
+        fprintf(stderr, "code: %d, reason: %s\n", error,                       \
+                cudaGetErrorString(error));                                    \
+        return -1;                                                             \
+    }                                                                          \
+}
+
+// This will output the proper CUDA error strings in the event that a CUDA host call returns an error
+#define checkCudaErrors(call)                                                            \
+{                                                                              \
+    const cudaError_t error = call;                                            \
+    if (error != cudaSuccess)                                                  \
+    {                                                                          \
+        fprintf(stderr, "Error: %s:%d, ", __FILE__, __LINE__);                 \
+        fprintf(stderr, "code: %d, reason: %s\n", error,                       \
+                cudaGetErrorString(error));                                    \
+        return -1;                                                                \
+    }                                                                          \
+}
+/*#define checkCudaErrors(err)           __checkCudaErrors (err, __FILE__, __LINE__)
+
+inline void __checkCudaErrors(cudaError err, const char *file, const int line)
+{
+    if (cudaSuccess != err)
+    {
+        fprintf(stderr, "%s(%i) : CUDA Runtime API error %d: %s.\n",
+                file, line, (int)err, cudaGetErrorString(err));
+        return;
+    }
+}
+*/
+
diff --git a/src/Matlab/CMakeLists.txt b/src/Matlab/CMakeLists.txt
new file mode 100755
index 0000000..b97f845
--- /dev/null
+++ b/src/Matlab/CMakeLists.txt
@@ -0,0 +1,147 @@
+project(regulariserMatlab)

+

+

+find_package(Matlab REQUIRED COMPONENTS MAIN_PROGRAM MX_LIBRARY ENG_LIBRARY )

+

+

+

+#C:\Users\ofn77899\Documents\Projects\CCPi\GitHub\CCPi-FISTA_Reconstruction\Core\regularisers_CPU

+# matlab_add_mex(

+    # NAME CPU_ROF

+    # SRC 

+      # ${CMAKE_SOURCE_DIR}/Matlab/mex_compile/regularisers_CPU/ROF_TV.c

+    # LINK_TO cilreg ${Matlab_LIBRARIES}

+    # )

+    

+# target_include_directories(CPU_ROF 

+   # PUBLIC ${CMAKE_SOURCE_DIR}/Core/regularisers_CPU

+   # ${CMAKE_SOURCE_DIR}/Core/regularisers_GPU

+   # ${CMAKE_SOURCE_DIR}/Core/inpainters_CPU

+   # ${CMAKE_SOURCE_DIR}/Core/

+   # ${MATLAB_INCLUDE_DIR})

+   

+   # matlab_add_mex(

+    # NAME CPU_TNV

+    # SRC 

+      # ${CMAKE_SOURCE_DIR}/Matlab/mex_compile/regularisers_CPU/TNV.c 

+    # LINK_TO cilreg ${Matlab_LIBRARIES}

+    # )

+    

+# target_include_directories(CPU_TNV 

+   # PUBLIC ${CMAKE_SOURCE_DIR}/Core/regularisers_CPU

+   # ${CMAKE_SOURCE_DIR}/Core/regularisers_GPU

+   # ${CMAKE_SOURCE_DIR}/Core/inpainters_CPU

+   # ${CMAKE_SOURCE_DIR}/Core/

+   # ${MATLAB_INCLUDE_DIR})

+   

+#set (CPU_MEX_FILES "regularisers_CPU/TNV.c;regularisers_CPU/ROF_TV.c")

+#set (MEX_TARGETS "CPU_TNV;CPU_ROF")

+#list(APPEND MEX_TARGETS "CPU_TNV")

+#list(APPEND MEX_TARGETS "CPU_ROF")

+

+file(GLOB CPU_MEX_FILES

+    "${CMAKE_SOURCE_DIR}/Matlab/mex_compile/regularisers_CPU/*.c"

+    #"${CMAKE_SOURCE_DIR}/Matlab/mex_compile/regularisers_GPU/*.c"

+)

+

+#message("CPU_MEX_FILES " ${CPU_MEX_FILES})

+

+list(LENGTH CPU_MEX_FILES num)

+

+

+MATH(EXPR num "${num}-1")

+#set(num "-1")

+message("found ${num} files")

+

+foreach(tgt RANGE 0 ${num})

+  message("number " ${tgt})

+  list(LENGTH CPU_MEX_FILES num2)

+  message("the list is ${num2}")

+  #list(GET CPU_TARGETS ${tgt} current_target)

+  list(GET CPU_MEX_FILES ${tgt} current_file_name)

+  get_filename_component(current_file ${current_file_name} NAME)

+  string(REGEX MATCH "(.+).c" match ${current_file})

+  if (NOT ${match} EQUAL "" )

+  set (current_target ${CMAKE_MATCH_1})

+  endif()

+  message("matlab_add_mex target " ${current_file} " and " ${current_target})

+  matlab_add_mex(

+    NAME ${current_target}

+    SRC 

+      ${current_file_name} 

+            #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/FGP_TV_core.c

+	    #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/SB_TV_core.c

+	    #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/TGV_core.c

+	    #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/Diffusion_core.c

+	    #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/Diffus4th_order_core.c

+	    #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/LLT_ROF_core.c

+            #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/ROF_TV_core.c

+            #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/FGP_dTV_core.c

+            #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/TNV_core.c

+            #${CMAKE_SOURCE_DIR}/Core/regularisers_CPU/utils.c

+	    #${CMAKE_SOURCE_DIR}/Core/inpainters_CPU/Diffusion_Inpaint_core.c

+	    #${CMAKE_SOURCE_DIR}/Core/inpainters_CPU/NonlocalMarching_Inpaint_core.c

+    LINK_TO cilreg ${Matlab_LIBRARIES}

+    )

+    

+target_include_directories(${current_target}

+   PUBLIC ${CMAKE_SOURCE_DIR}/Core/regularisers_CPU

+   ${CMAKE_SOURCE_DIR}/Core/regularisers_GPU

+   ${CMAKE_SOURCE_DIR}/Core/inpainters_CPU

+   ${CMAKE_SOURCE_DIR}/Core/

+   ${MATLAB_INCLUDE_DIR})

+   set_property(TARGET ${current_target} PROPERTY C_STANDARD 99)

+   list(APPEND CPU_MEX_TARGETS ${current_target})

+   INSTALL(TARGETS ${current_target} DESTINATION "${MATLAB_DEST}")

+endforeach()

+   

+add_custom_target(MatlabWrapper DEPENDS ${CPU_MEX_TARGETS})

+

+if (BUILD_CUDA)

+    find_package(CUDA)

+    if (CUDA_FOUND)

+      file(GLOB GPU_MEX_FILES

+        "${CMAKE_SOURCE_DIR}/Matlab/mex_compile/regularisers_GPU/*.cpp"

+      )

+

+      list(LENGTH GPU_MEX_FILES num)

+message("number of GPU files  " ${num})

+

+      MATH(EXPR num "${num}-1")

+    #set(num "-1")

+

+      foreach(tgt RANGE ${num})

+        message("number " ${tgt})

+  list(LENGTH GPU_MEX_FILES num2)

+  message("the list is ${num2}")

+  #list(GET CPU_TARGETS ${tgt} current_target)

+  list(GET GPU_MEX_FILES ${tgt} current_file_name)

+  get_filename_component(current_file ${current_file_name} NAME)

+  string(REGEX MATCH "(.+).c" match ${current_file})

+  if (NOT ${match} EQUAL "" )

+  set (current_target ${CMAKE_MATCH_1})

+  endif()

+  message("matlab_add_mex target " ${current_file} " and " ${current_target})

+        message("matlab_add_mex " ${current_target})

+        matlab_add_mex(

+          NAME ${current_target}

+          SRC 

+            ${current_file_name} 

+          LINK_TO cilregcuda ${Matlab_LIBRARIES}

+          )

+        

+        target_include_directories(${current_target}

+        PUBLIC ${CMAKE_SOURCE_DIR}/Core/regularisers_CPU

+               ${CMAKE_SOURCE_DIR}/Core/regularisers_GPU

+               ${CMAKE_SOURCE_DIR}/Core/inpainters_CPU

+               ${CMAKE_SOURCE_DIR}/Core/

+               ${MATLAB_INCLUDE_DIR})

+       

+        list(APPEND GPU_MEX_TARGETS ${current_target})

+        INSTALL(TARGETS ${current_target} DESTINATION "${MATLAB_DEST}")

+      endforeach()

+       

+      add_custom_target(MatlabWrapperGPU DEPENDS ${GPU_MEX_TARGETS})

+      

+    endif()

+endif()

diff --git a/src/Matlab/mex_compile/compileCPU_mex_Linux.m b/src/Matlab/mex_compile/compileCPU_mex_Linux.m
new file mode 100644
index 0000000..72a828e
--- /dev/null
+++ b/src/Matlab/mex_compile/compileCPU_mex_Linux.m
@@ -0,0 +1,81 @@
+% execute this mex file on Linux in Matlab once
+
+fsep = '/';
+
+pathcopyFrom = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'regularisers_CPU'], 1i);
+pathcopyFrom1 = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'CCPiDefines.h'], 1i);
+pathcopyFrom2 = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'inpainters_CPU'], 1i);
+
+copyfile(pathcopyFrom, 'regularisers_CPU');
+copyfile(pathcopyFrom1, 'regularisers_CPU');
+copyfile(pathcopyFrom2, 'regularisers_CPU');
+
+cd regularisers_CPU
+
+Pathmove = sprintf(['..' fsep 'installed' fsep], 1i);
+
+fprintf('%s \n', '<<<<<<<<<<<Compiling CPU regularisers>>>>>>>>>>>>>');
+
+fprintf('%s \n', 'Compiling ROF-TV...');
+mex ROF_TV.c ROF_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('ROF_TV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling FGP-TV...');
+mex FGP_TV.c FGP_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('FGP_TV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling SB-TV...');
+mex SB_TV.c SB_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('SB_TV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling dFGP-TV...');
+mex FGP_dTV.c FGP_dTV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('FGP_dTV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling TNV...');
+mex TNV.c TNV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('TNV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling NonLinear Diffusion...');
+mex NonlDiff.c Diffusion_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('NonlDiff.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...');
+mex Diffusion_4thO.c Diffus4th_order_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('Diffusion_4thO.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling TGV...');
+mex TGV.c TGV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('TGV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling ROF-LLT...');
+mex LLT_ROF.c LLT_ROF_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('LLT_ROF.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling NonLocal-TV...');
+mex PatchSelect.c PatchSelect_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+mex Nonlocal_TV.c Nonlocal_TV_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('Nonlocal_TV.mex*',Pathmove);
+movefile('PatchSelect.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling additional tools...');
+mex TV_energy.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('TV_energy.mex*',Pathmove);
+
+%############Inpainters##############%
+fprintf('%s \n', 'Compiling Nonlinear/Linear diffusion inpainting...');
+mex NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('NonlDiff_Inp.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling Nonlocal marching method for inpainting...');
+mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c CFLAGS="\$CFLAGS -fopenmp -Wall -std=c99" LDFLAGS="\$LDFLAGS -fopenmp"
+movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
+
+delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* LLT_ROF_core* CCPiDefines.h
+delete PatchSelect_core* Nonlocal_TV_core*
+delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
+fprintf('%s \n', '<<<<<<< Regularisers successfully compiled! >>>>>>>');
+
+pathA2 = sprintf(['..' fsep '..' fsep], 1i);
+cd(pathA2);
+cd demos
diff --git a/src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m b/src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
new file mode 100644
index 0000000..6f7541c
--- /dev/null
+++ b/src/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
@@ -0,0 +1,135 @@
+% execute this mex file on Windows in Matlab once
+
+% >>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+% I've been able to compile on Windows 7 with MinGW and Matlab 2016b, however, 
+% not sure if openmp is enabled after the compilation. 
+
+% Here I present two ways how software can be compiled, if you have some
+% other suggestions/remarks please contact me at dkazanc@hotmail.com 
+% >>>>>>>>>>>>>>>>>>>>>>>>>>>>>
+
+fsep = '/';
+
+pathcopyFrom = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'regularisers_CPU'], 1i);
+pathcopyFrom1 = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'CCPiDefines.h'], 1i);
+pathcopyFrom2 = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'inpainters_CPU'], 1i);
+
+copyfile(pathcopyFrom, 'regularisers_CPU');
+copyfile(pathcopyFrom1, 'regularisers_CPU');
+copyfile(pathcopyFrom2, 'regularisers_CPU');
+
+cd regularisers_CPU
+
+Pathmove = sprintf(['..' fsep 'installed' fsep], 1i);
+
+fprintf('%s \n', '<<<<<<<<<<<Compiling CPU regularisers>>>>>>>>>>>>>');
+
+fprintf('%s \n', 'Compiling ROF-TV...');
+mex ROF_TV.c ROF_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('ROF_TV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling FGP-TV...');
+mex FGP_TV.c FGP_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('FGP_TV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling SB-TV...');
+mex SB_TV.c SB_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('SB_TV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling dFGP-TV...');
+mex FGP_dTV.c FGP_dTV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('FGP_dTV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling TNV...');
+mex TNV.c TNV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('TNV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling NonLinear Diffusion...');
+mex NonlDiff.c Diffusion_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('NonlDiff.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...');
+mex Diffusion_4thO.c Diffus4th_order_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('Diffusion_4thO.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling TGV...');
+mex TGV.c TGV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('TGV.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling ROF-LLT...');
+mex LLT_ROF.c LLT_ROF_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('LLT_ROF.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling NonLocal-TV...');
+mex PatchSelect.c PatchSelect_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+mex Nonlocal_TV.c Nonlocal_TV_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('Nonlocal_TV.mex*',Pathmove);
+movefile('PatchSelect.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling additional tools...');
+mex TV_energy.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('TV_energy.mex*',Pathmove);
+
+%############Inpainters##############%
+fprintf('%s \n', 'Compiling Nonlinear/Linear diffusion inpainting...');
+mex NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('NonlDiff_Inp.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling Nonlocal marching method for inpaiting...');
+mex NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c COMPFLAGS="\$COMPFLAGS -fopenmp -Wall -std=c99"
+movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
+
+
+%%
+%%% The second approach to compile using TDM-GCC which follows this
+%%% discussion:
+%%% https://uk.mathworks.com/matlabcentral/answers/279171-using-mingw-compiler-and-open-mp#comment_359122
+%%% 1. Install TDM-GCC independently from http://tdm-gcc.tdragon.net/ (I installed 5.1.0)
+%%% Install openmp version: http://sourceforge.net/projects/tdm-gcc/files/TDM-GCC%205%20series/5.1.0-tdm64-1/gcc-5.1.0-tdm64-1-openmp.zip/download
+%%% 2. Link til libgomp.a in that installation when compilling your mex file.
+
+%%% assuming you unzipped TDM GCC (OpenMp) in folder TDMGCC on C drive, uncomment
+%%% bellow
+% fprintf('%s \n', 'Compiling CPU regularisers...');
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" ROF_TV.c ROF_TV_core.c utils.c
+% movefile('ROF_TV.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" FGP_TV.c FGP_TV_core.c utils.c
+% movefile('FGP_TV.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" SB_TV.c SB_TV_core.c utils.c
+% movefile('SB_TV.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" FGP_dTV.c FGP_dTV_core.c utils.c
+% movefile('FGP_dTV.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TNV.c TNV_core.c utils.c
+% movefile('TNV.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" NonlDiff.c Diffusion_core.c utils.c
+% movefile('NonlDiff.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" Diffusion_4thO.c Diffus4th_order_core.c utils.c
+% movefile('Diffusion_4thO.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TGV.c TGV_core.c utils.c
+% movefile('TGV.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" LLT_ROF.c LLT_ROF_core.c utils.c
+% movefile('LLT_ROF.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" PatchSelect.c PatchSelect_core.c utils.c
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" Nonlocal_TV.c Nonlocal_TV_core.c utils.c
+% movefile('Nonlocal_TV.mex*',Pathmove);
+% movefile('PatchSelect.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" TV_energy.c utils.c
+% movefile('TV_energy.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" NonlDiff_Inp.c Diffusion_Inpaint_core.c utils.c
+% movefile('NonlDiff_Inp.mex*',Pathmove);
+% mex C:\TDMGCC\lib\gcc\x86_64-w64-mingw32\5.1.0\libgomp.a CXXFLAGS="$CXXFLAGS -std=c++11 -fopenmp" NonlocalMarching_Inpaint.c NonlocalMarching_Inpaint_core.c utils.c
+% movefile('NonlocalMarching_Inpaint.mex*',Pathmove);
+
+
+delete SB_TV_core* ROF_TV_core* FGP_TV_core* FGP_dTV_core* TNV_core* utils* Diffusion_core* Diffus4th_order_core* TGV_core* CCPiDefines.h
+delete PatchSelect_core* Nonlocal_TV_core*
+delete Diffusion_Inpaint_core* NonlocalMarching_Inpaint_core*
+fprintf('%s \n', 'Regularisers successfully compiled!');
+
+
+%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%pathA2 = sprintf(['..' fsep '..' fsep], 1i);
+%cd(pathA2);
+%cd demos
diff --git a/src/Matlab/mex_compile/compileGPU_mex.m b/src/Matlab/mex_compile/compileGPU_mex.m
new file mode 100644
index 0000000..dd1475c
--- /dev/null
+++ b/src/Matlab/mex_compile/compileGPU_mex.m
@@ -0,0 +1,74 @@
+% execute this mex file in Matlab once
+
+%>>>>>>>>>>>>>>>>>Important<<<<<<<<<<<<<<<<<<<
+% In order to compile CUDA modules one needs to have nvcc-compiler
+% installed (see CUDA SDK), check it under MATLAB with !nvcc --version
+
+% In the code bellow we provide a full explicit path to nvcc compiler 
+% ! paths to matlab and CUDA sdk can be different, modify accordingly !
+
+% Tested on Ubuntu 18.04/MATLAB 2016b/cuda10.0/gcc7.3
+
+% Installation HAS NOT been tested on Windows, please you Cmake build or
+% modify the code bellow accordingly
+fsep = '/';
+
+pathcopyFrom = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'regularisers_GPU'], 1i);
+pathcopyFrom1 = sprintf(['..' fsep '..' fsep '..' fsep 'Core' fsep 'CCPiDefines.h'], 1i);
+
+copyfile(pathcopyFrom, 'regularisers_GPU');
+copyfile(pathcopyFrom1, 'regularisers_GPU');
+
+cd regularisers_GPU
+
+Pathmove = sprintf(['..' fsep 'installed' fsep], 1i);
+
+fprintf('%s \n', '<<<<<<<<<<<Compiling GPU regularisers (CUDA)>>>>>>>>>>>>>');
+
+fprintf('%s \n', 'Compiling ROF-TV...');
+!/usr/local/cuda/bin/nvcc -O0 -c TV_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu ROF_TV_GPU.cpp TV_ROF_GPU_core.o
+movefile('ROF_TV_GPU.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling FGP-TV...');
+!/usr/local/cuda/bin/nvcc -O0 -c TV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu FGP_TV_GPU.cpp TV_FGP_GPU_core.o
+movefile('FGP_TV_GPU.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling SB-TV...');
+!/usr/local/cuda/bin/nvcc -O0 -c TV_SB_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu SB_TV_GPU.cpp TV_SB_GPU_core.o
+movefile('SB_TV_GPU.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling TGV...');
+!/usr/local/cuda/bin/nvcc -O0 -c TGV_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu TGV_GPU.cpp TGV_GPU_core.o
+movefile('TGV_GPU.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling dFGP-TV...');
+!/usr/local/cuda/bin/nvcc -O0 -c dTV_FGP_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu FGP_dTV_GPU.cpp dTV_FGP_GPU_core.o
+movefile('FGP_dTV_GPU.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling NonLinear Diffusion...');
+!/usr/local/cuda/bin/nvcc -O0 -c NonlDiff_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu NonlDiff_GPU.cpp NonlDiff_GPU_core.o
+movefile('NonlDiff_GPU.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling Anisotropic diffusion of higher order...');
+!/usr/local/cuda/bin/nvcc -O0 -c Diffus_4thO_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu Diffusion_4thO_GPU.cpp Diffus_4thO_GPU_core.o
+movefile('Diffusion_4thO_GPU.mex*',Pathmove);
+
+fprintf('%s \n', 'Compiling ROF-LLT...');
+!/usr/local/cuda/bin/nvcc -O0 -c LLT_ROF_GPU_core.cu -Xcompiler -fPIC -I~/SOFT/MATLAB9/extern/include/
+mex -g -I/usr/local/cuda-10.0/include -L/usr/local/cuda-10.0/lib64 -lcudart -lcufft -lmwgpu LLT_ROF_GPU.cpp LLT_ROF_GPU_core.o
+movefile('LLT_ROF_GPU.mex*',Pathmove);
+
+
+delete TV_ROF_GPU_core* TV_FGP_GPU_core* TV_SB_GPU_core* dTV_FGP_GPU_core* NonlDiff_GPU_core* Diffus_4thO_GPU_core* TGV_GPU_core* LLT_ROF_GPU_core* CCPiDefines.h
+fprintf('%s \n', 'All successfully compiled!');
+
+pathA2 = sprintf(['..' fsep '..' fsep], 1i);
+cd(pathA2);
+cd demos
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/installed/MEXed_files_location.txt b/src/Matlab/mex_compile/installed/MEXed_files_location.txt
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/src/Matlab/mex_compile/installed/MEXed_files_location.txt
diff --git a/src/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c b/src/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c
new file mode 100644
index 0000000..66ea9be
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c
@@ -0,0 +1,77 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "Diffus4th_order_core.h"
+
+/* C-OMP implementation of fourth-order diffusion scheme [1] for piecewise-smooth recovery (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED]
+ * 3. Edge-preserving parameter (sigma) [REQUIRED]
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended [OPTIONAL, default 300]
+ * 5. tau - time-marching step for the explicit scheme [OPTIONAL, default 0.015]
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter_numb;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    float *Input, *Output=NULL, lambda, tau, sigma;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    sigma = (float) mxGetScalar(prhs[2]); /* Edge-preserving parameter */
+    iter_numb = 300; /* iterations number */
+    tau = 0.01; /* marching step parameter */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant");
+    if ((nrhs == 4) || (nrhs == 5))  iter_numb = (int) mxGetScalar(prhs[3]); /* iterations number */
+    if (nrhs == 5)  tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    Diffus4th_CPU_main(Input, Output, lambda, sigma, iter_numb, tau, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/FGP_TV.c b/src/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
new file mode 100644
index 0000000..642362f
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
@@ -0,0 +1,97 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "FGP_TV_core.h"
+
+/* C-OMP implementation of FGP-TV [1] denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume
+ * 2. lambdaPar - regularization parameter
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default)
+ * 7. print information: 0 (off) or 1 (on)
+ *
+ * Output:
+ * [1] Filtered/regularized image
+ *
+ * This function is based on the Matlab's code and paper by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ */
+
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, printswitch, nonneg;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    float *Input, *Output=NULL, lambda, epsil;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, print switch");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 300; /* default iterations number */
+    epsil = 0.0001; /* default tolerance constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    nonneg = 0; /* default nonnegativity switch, off - 0 */
+    printswitch = 0; /*default print is switched, off - 0 */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7))  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+        mxFree(penalty_type);
+    }
+    if ((nrhs == 6) || (nrhs == 7))  {
+        nonneg = (int) mxGetScalar(prhs[5]);
+        if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
+    }
+    if (nrhs == 7)  {
+        printswitch = (int) mxGetScalar(prhs[6]);
+        if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0");
+    }
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */
+    TV_FGP_CPU_main(Input, Output, lambda, iter, epsil, methTV, nonneg, printswitch, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c b/src/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c
new file mode 100644
index 0000000..1a0c070
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c
@@ -0,0 +1,114 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "FGP_dTV_core.h"
+
+/* C-OMP implementation of FGP-dTV [1,2] denoising/regularization model (2D/3D case)
+ * which employs structural similarity of the level sets of two images/volumes, see [1,2]
+ * The current implementation updates image 1 while image 2 is being fixed.
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. Additional reference image/volume of the same dimensions as (1) [REQUIRED]
+ * 3. lambdaPar - regularization parameter [REQUIRED]
+ * 4. Number of iterations [OPTIONAL]
+ * 5. eplsilon: tolerance constant [OPTIONAL]
+ * 6. eta: smoothing constant to calculate gradient of the reference [OPTIONAL] * 
+ * 7. TV-type: methodTV - 'iso' (0) or 'l1' (1) [OPTIONAL]
+ * 8. nonneg: 'nonnegativity (0 is OFF by default) [OPTIONAL]
+ * 9. print information: 0 (off) or 1 (on) [OPTIONAL]
+ *
+ * Output:
+ * [1] Filtered/regularized image/volume
+ *
+ * This function is based on the Matlab's codes and papers by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ * [2] M. J. Ehrhardt and M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), pp. 1084–1106
+ */
+
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, printswitch, nonneg;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    const mwSize *dim_array2;    
+    float *Input, *InputRef, *Output=NULL, lambda, epsil, eta;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    dim_array2 = mxGetDimensions(prhs[1]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 3) || (nrhs > 9)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Reference(2D/3D), Regularization parameter, iterations number, tolerance, smoothing constant, penalty type ('iso' or 'l1'), nonnegativity switch, print switch");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    InputRef  = (float *) mxGetData(prhs[1]); /* reference image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[2]); /* regularization parameter */
+    iter = 300; /* default iterations number */
+    epsil = 0.0001; /* default tolerance constant */
+    eta = 0.01; /* default smoothing constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    nonneg = 0; /* default nonnegativity switch, off - 0 */
+    printswitch = 0; /*default print is switched, off - 0 */
+    
+        
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if (mxGetClassID(prhs[1]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    if (number_of_dims == 2) { if ((dimX != dim_array2[0]) || (dimY != dim_array2[1])) mexErrMsgTxt("The input images have different dimensionalities");}
+    if (number_of_dims == 3) { if ((dimX != dim_array2[0]) || (dimY != dim_array2[1]) || (dimZ != dim_array2[2])) mexErrMsgTxt("The input volumes have different dimensionalities");}   
+    
+    
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8) || (nrhs == 9))  iter = (int) mxGetScalar(prhs[3]); /* iterations number */
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8) || (nrhs == 9))  epsil =  (float) mxGetScalar(prhs[4]); /* tolerance constant */
+    if ((nrhs == 6) || (nrhs == 7) || (nrhs == 8) || (nrhs == 9))  {
+    eta =  (float) mxGetScalar(prhs[5]); /* smoothing constant for the gradient of InputRef */
+    }
+    if ((nrhs == 7) || (nrhs == 8) || (nrhs == 9))  {        
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[6]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+        mxFree(penalty_type);
+    }    
+    if ((nrhs == 8) || (nrhs == 9))  {
+        nonneg = (int) mxGetScalar(prhs[7]);
+        if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
+    }
+    if (nrhs == 9)  {
+        printswitch = (int) mxGetScalar(prhs[8]);
+        if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0");
+    }    
+   
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */
+    dTV_FGP_CPU_main(Input, InputRef, Output, lambda, iter, epsil, eta, methTV, nonneg, printswitch, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c b/src/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
new file mode 100644
index 0000000..ab45446
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
@@ -0,0 +1,82 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "LLT_ROF_core.h"
+
+/* C-OMP implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+* 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iterationsNumb;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;    
+    float *Input, *Output=NULL, lambdaROF, lambdaLLT, tau;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter (ROF), Regularisation parameter (LTT), iterations number, time-marching parameter");
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambdaROF =  (float) mxGetScalar(prhs[1]); /* ROF regularization parameter */
+    lambdaLLT =  (float) mxGetScalar(prhs[2]); /* ROF regularization parameter */    
+    iterationsNumb = 250;
+    tau =  0.0025;
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
+    if ((nrhs == 4) || (nrhs == 5)) iterationsNumb =  (int) mxGetScalar(prhs[3]); /* iterations number */    
+    if (nrhs == 5) tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */  
+        
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));                        
+    }    
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));   
+  
+    LLT_ROF_CPU_main(Input, Output, lambdaROF, lambdaLLT, iterationsNumb, tau, dimX, dimY, dimZ);    
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/NonlDiff.c b/src/Matlab/mex_compile/regularisers_CPU/NonlDiff.c
new file mode 100644
index 0000000..ec35b8b
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/NonlDiff.c
@@ -0,0 +1,89 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "Diffusion_core.h"
+
+/* C-OMP implementation of linear and nonlinear diffusion with the regularisation model [1] (2D/3D case)
+ * The minimisation is performed using explicit scheme.
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended  [OPTIONAL parameter]
+ * 5. tau - time-marching step for explicit scheme [OPTIONAL parameter]
+ * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight [OPTIONAL parameter]
+ *
+ * Output:
+ * [1] Regularized image/volume
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter_numb, penaltytype;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;   
+    
+    float *Input, *Output=NULL, lambda, tau, sigma;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    sigma = (float) mxGetScalar(prhs[2]); /* Edge-preserving parameter */
+    iter_numb = 300; /* iterations number */
+    tau = 0.025; /* marching step parameter */
+    penaltytype = 1; /* Huber penalty by default */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if ((nrhs < 3) || (nrhs > 6)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant, penalty type - Huber, PM or Tukey");
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6))  iter_numb = (int) mxGetScalar(prhs[3]); /* iterations number */
+    if ((nrhs == 5) || (nrhs == 6))  tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */
+    if (nrhs == 6)  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[5]); /* Huber, PM or Tukey 'Huber' is the default */
+        if ((strcmp(penalty_type, "Huber") != 0) && (strcmp(penalty_type, "PM") != 0) && (strcmp(penalty_type, "Tukey") != 0)) mexErrMsgTxt("Choose penalty: 'Huber', 'PM' or 'Tukey',");
+        if (strcmp(penalty_type, "Huber") == 0)  penaltytype = 1;  /* enable 'Huber' penalty */
+        if (strcmp(penalty_type, "PM") == 0)  penaltytype = 2;  /* enable Perona-Malik penalty */
+        if (strcmp(penalty_type, "Tukey") == 0)  penaltytype = 3;  /* enable Tikey Biweight penalty */
+        mxFree(penalty_type);
+    }    
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    Diffusion_CPU_main(Input, Output, lambda, sigma, iter_numb, tau, penaltytype, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c b/src/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c
new file mode 100644
index 0000000..9833392
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c
@@ -0,0 +1,103 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "Diffusion_Inpaint_core.h"
+
+/* C-OMP implementation of linear and nonlinear diffusion [1,2] for inpainting task (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Image/volume to inpaint
+ * 2. Inpainting Mask of the same size as (1) in 'unsigned char' format  (ones mark the region to inpaint, zeros belong to the data)
+ * 3. lambda - regularization parameter
+ * 4. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 5. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 6. tau - time-marching step for explicit scheme
+ * 7. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight
+ *
+ * Output:
+ * [1] Inpainted image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter_numb, penaltytype, i, inpaint_elements;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;   
+    const mwSize *dim_array2;   
+    
+    float *Input, *Output=NULL, lambda, tau, sigma;
+    unsigned char *Mask;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    dim_array2 = mxGetDimensions(prhs[1]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    Mask  = (unsigned char *) mxGetData(prhs[1]); /* MASK */
+    lambda =  (float) mxGetScalar(prhs[2]); /* regularization parameter */
+    sigma = (float) mxGetScalar(prhs[3]); /* Edge-preserving parameter */
+    iter_numb = 300; /* iterations number */
+    tau = 0.025; /* marching step parameter */
+    penaltytype = 1; /* Huber penalty by default */    
+  
+    if ((nrhs < 4) || (nrhs > 7)) mexErrMsgTxt("At least 4 parameters is required, all parameters are: Image(2D/3D), Mask(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant, penalty type - Huber, PM or Tukey");
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7))  iter_numb = (int) mxGetScalar(prhs[4]); /* iterations number */
+    if ((nrhs == 6) || (nrhs == 7))  tau =  (float) mxGetScalar(prhs[5]); /* marching step parameter */
+    if (nrhs == 7)  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[6]); /* Huber, PM or Tukey 'Huber' is the default */
+        if ((strcmp(penalty_type, "Huber") != 0) && (strcmp(penalty_type, "PM") != 0) && (strcmp(penalty_type, "Tukey") != 0)) mexErrMsgTxt("Choose penalty: 'Huber', 'PM' or 'Tukey',");
+        if (strcmp(penalty_type, "Huber") == 0)  penaltytype = 1;  /* enable 'Huber' penalty */
+        if (strcmp(penalty_type, "PM") == 0)  penaltytype = 2;  /* enable Perona-Malik penalty */
+        if (strcmp(penalty_type, "Tukey") == 0)  penaltytype = 3;  /* enable Tikey Biweight penalty */
+        mxFree(penalty_type);
+    }    
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if (mxGetClassID(prhs[1]) != mxUINT8_CLASS) {mexErrMsgTxt("The mask must be in uint8 precision");}
+    
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        if ((dimX != dim_array2[0]) || (dimY != dim_array2[1])) mexErrMsgTxt("Input image and the provided mask are of different dimensions!");
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }    
+    if (number_of_dims == 3) {
+        if ((dimX != dim_array2[0]) || (dimY != dim_array2[1]) || (dimZ != dim_array2[2])) mexErrMsgTxt("Input image and the provided mask are of different dimensions!");
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    }    
+    
+    inpaint_elements = 0;
+    for (i=0; i<(int)(dimY*dimX*dimZ); i++) if (Mask[i] == 1) inpaint_elements++;
+    if (inpaint_elements == 0) mexErrMsgTxt("The mask is full of zeros, nothing to inpaint");        
+    Diffusion_Inpaint_CPU_main(Input, Mask, Output, lambda, sigma, iter_numb, tau, penaltytype, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c b/src/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c
new file mode 100644
index 0000000..b3f2c98
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c
@@ -0,0 +1,84 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "NonlocalMarching_Inpaint_core.h"
+
+/* C-OMP implementation of Nonlocal Vertical Marching inpainting method (2D case)
+ * The method is heuristic but computationally efficent (especially for larger images).
+ * It developed specifically to smoothly inpaint horizontal or inclined missing data regions in sinograms
+ * The method WILL not work satisfactory if you have lengthy vertical stripes of missing data
+ *
+ * Input:
+ * 1. 2D image or sinogram [REQUIRED]
+ * 2. Mask of the same size as A in 'unsigned char' format  (ones mark the region to inpaint, zeros belong to the data) [REQUIRED]
+ * 3. Linear increment to increase searching window size in iterations, values from 1-3 is a good choice [OPTIONAL, default 1]
+ * 4. Number of iterations [OPTIONAL, default - calculate based on the mask]
+ *
+ * Output:
+ * 1. Inpainted sinogram  
+ * 2. updated mask
+ * Reference: TBA
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iterations, SW_increment;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    const mwSize *dim_array2;
+    
+    float *Input, *Output=NULL;
+    unsigned char *Mask, *Mask_upd=NULL;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    dim_array2 = mxGetDimensions(prhs[1]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    Mask  = (unsigned char *) mxGetData(prhs[1]); /* MASK */    
+    SW_increment = 1;
+    iterations = 0;
+            
+    if ((nrhs < 2) || (nrhs > 4)) mexErrMsgTxt("At least 4 parameters is required, all parameters are: Image(2D/3D), Mask(2D/3D), Linear increment, Iterations number");
+    if ((nrhs == 3) || (nrhs == 4))  SW_increment =  (int) mxGetScalar(prhs[2]); /* linear increment */
+    if ((nrhs == 4))  iterations =  (int) mxGetScalar(prhs[3]); /* iterations number */
+       
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if (mxGetClassID(prhs[1]) != mxUINT8_CLASS) {mexErrMsgTxt("The mask must be in uint8 precision");}    
+    
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        if ((dimX != dim_array2[0]) || (dimY != dim_array2[1])) mexErrMsgTxt("Input image and the provided mask are of different dimensions!");
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+        Mask_upd = (unsigned char*)mxGetPr(plhs[1] = mxCreateNumericArray(2, dim_array, mxUINT8_CLASS, mxREAL));
+    }    
+    if (number_of_dims == 3) {
+        mexErrMsgTxt("Currently 2D supported only");        
+    }           
+    NonlocalMarching_Inpaint_main(Input, Mask, Output, Mask_upd, SW_increment, iterations, 0, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c b/src/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
new file mode 100644
index 0000000..014c0a0
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
@@ -0,0 +1,88 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC and Diamond Light Source Ltd. 
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ * Copyright 2018 Diamond Light Source Ltd. 
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "matrix.h"
+#include "mex.h"
+#include "Nonlocal_TV_core.h"
+
+#define EPS 1.0000e-9
+
+/* Matlab wrapper for C-OMP implementation of non-local regulariser
+ * Weights and associated indices must be given as an input.
+ * Gauss-Seidel fixed point iteration requires ~ 3 iterations, so the main effort
+ * goes in pre-calculation of weights and selection of patches
+ *
+ *
+ * Input Parameters:
+ * 1. 2D/3D grayscale image/volume
+ * 2. AR_i - indeces of i neighbours
+ * 3. AR_j - indeces of j neighbours
+ * 4. AR_k - indeces of k neighbours (0 - for 2D case)
+ * 5. Weights_ij(k) - associated weights 
+ * 6. regularisation parameter
+ * 7. iterations number 
+ 
+ * Output:
+ * 1. denoised image/volume 	
+ * Elmoataz, Abderrahim, Olivier Lezoray, and Sébastien Bougleux. "Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing." IEEE Trans. Image Processing 17, no. 7 (2008): 1047-1060.
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+{
+    long number_of_dims,  dimX, dimY, dimZ;
+    int IterNumb, NumNeighb = 0;
+    unsigned short *H_i, *H_j, *H_k;
+    const int  *dim_array;
+    const int  *dim_array2;
+    float *A_orig, *Output=NULL, *Weights, lambda;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    dim_array2 = mxGetDimensions(prhs[1]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    A_orig  = (float *) mxGetData(prhs[0]); /* a 2D image or a set of 2D images (3D stack) */
+    H_i  = (unsigned short *) mxGetData(prhs[1]); /* indeces of i neighbours */
+    H_j  = (unsigned short *) mxGetData(prhs[2]); /* indeces of j neighbours */
+    H_k  = (unsigned short *) mxGetData(prhs[3]); /* indeces of k neighbours */
+    Weights = (float *) mxGetData(prhs[4]); /* weights for patches */
+    lambda = (float) mxGetScalar(prhs[5]); /* regularisation parameter */
+    IterNumb = (int) mxGetScalar(prhs[6]); /* the number of iterations */
+ 
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];   
+         
+    /*****2D INPUT *****/
+    if (number_of_dims == 2) {
+        dimZ = 0;   
+        NumNeighb = dim_array2[2];
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));  
+        }
+    /*****3D INPUT *****/
+    /****************************************************/
+    if (number_of_dims == 3) {
+        NumNeighb = dim_array2[3];
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    
+    /* run the main function here */
+    Nonlocal_TV_CPU_main(A_orig, Output, H_i, H_j, H_k, Weights, dimX, dimY, dimZ, NumNeighb, lambda, IterNumb);
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/PatchSelect.c b/src/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
new file mode 100644
index 0000000..f942539
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
@@ -0,0 +1,92 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC and Diamond Light Source Ltd. 
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ * Copyright 2018 Diamond Light Source Ltd. 
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "matrix.h"
+#include "mex.h"
+#include "PatchSelect_core.h"
+
+/* C-OMP implementation of non-local weight pre-calculation for non-local priors
+ * Weights and associated indices are stored into pre-allocated arrays and passed
+ * to the regulariser
+ *
+ *
+ * Input Parameters:
+ * 1. 2D/3D grayscale image/volume
+ * 2. Searching window (half-size of the main bigger searching window, e.g. 11)
+ * 3. Similarity window (half-size of the patch window, e.g. 2)
+ * 4. The number of neighbours to take (the most prominent after sorting neighbours will be taken)
+ * 5. noise-related parameter to calculate non-local weights
+ *
+ * Output [2D]:
+ * 1. AR_i - indeces of i neighbours
+ * 2. AR_j - indeces of j neighbours
+ * 3. Weights_ij - associated weights
+ *
+ * Output [3D]:
+ * 1. AR_i - indeces of i neighbours
+ * 2. AR_j - indeces of j neighbours
+ * 3. AR_k - indeces of j neighbours
+ * 4. Weights_ijk - associated weights
+ */
+/**************************************************/
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+{
+    int number_of_dims,  SearchWindow, SimilarWin, NumNeighb;
+    mwSize dimX, dimY, dimZ;
+    unsigned short *H_i=NULL, *H_j=NULL, *H_k=NULL;
+    const int  *dim_array;
+    float *A, *Weights = NULL, h;
+    int dim_array2[3]; /* for 2D data */
+    int dim_array3[4]; /* for 3D data */
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    A  = (float *) mxGetData(prhs[0]); /* a 2D or 3D image/volume */
+    SearchWindow = (int) mxGetScalar(prhs[1]);    /* Large Searching window */
+    SimilarWin = (int) mxGetScalar(prhs[2]);    /* Similarity window (patch-search)*/
+    NumNeighb = (int) mxGetScalar(prhs[3]); /* the total number of neighbours to take */
+    h = (float) mxGetScalar(prhs[4]); /* NLM parameter */
+
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    dim_array2[0] = dimX; dim_array2[1] = dimY; dim_array2[2] = NumNeighb;  /* 2D case */
+    dim_array3[0] = dimX; dim_array3[1] = dimY; dim_array3[2] = dimZ; dim_array3[3] = NumNeighb;  /* 3D case */
+    
+    /****************2D INPUT ***************/
+    if (number_of_dims == 2) {
+        dimZ = 0;               
+        H_i = (unsigned short*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL));
+        H_j = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(3, dim_array2, mxUINT16_CLASS, mxREAL));
+        Weights = (float*)mxGetPr(plhs[2] = mxCreateNumericArray(3, dim_array2, mxSINGLE_CLASS, mxREAL));
+        }
+    /****************3D INPUT ***************/
+    if (number_of_dims == 3) {        
+        H_i = (unsigned short*)mxGetPr(plhs[0] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
+        H_j = (unsigned short*)mxGetPr(plhs[1] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
+        H_k = (unsigned short*)mxGetPr(plhs[2] = mxCreateNumericArray(4, dim_array3, mxUINT16_CLASS, mxREAL));
+        Weights = (float*)mxGetPr(plhs[3] = mxCreateNumericArray(4, dim_array3, mxSINGLE_CLASS, mxREAL));        
+    }
+    
+    PatchSelect_CPU_main(A, H_i, H_j, H_k, Weights, (long)(dimX), (long)(dimY), (long)(dimZ), SearchWindow, SimilarWin, NumNeighb, h, 0); 
+    
+ }
diff --git a/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c b/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
new file mode 100644
index 0000000..55ef2b1
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
@@ -0,0 +1,77 @@
+
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "ROF_TV_core.h"
+
+/* ROF-TV denoising/regularization model [1] (2D/3D case)
+ * (MEX wrapper for MATLAB)
+ * 
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED]
+ * 3. Number of iterations, for explicit scheme >= 150 is recommended  [REQUIRED]
+ * 4. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+ *
+ * D. Kazantsev, 2016-18
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter_numb;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array_i;
+    float *Input, *Output=NULL, lambda, tau;    
+    
+    dim_array_i = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter_numb =  (int) mxGetScalar(prhs[2]); /* iterations number */
+    tau =  (float) mxGetScalar(prhs[3]); /* marching step parameter */  
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if(nrhs != 4) mexErrMsgTxt("Four inputs reqired: Image(2D,3D), regularization parameter, iterations number,  marching step constant");
+    /*Handling Matlab output data*/
+    dimX = dim_array_i[0]; dimY = dim_array_i[1]; dimZ = dim_array_i[2];        
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array_i, mxSINGLE_CLASS, mxREAL));          
+    }    
+    if (number_of_dims == 3) {
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array_i, mxSINGLE_CLASS, mxREAL));
+    }
+     
+    TV_ROF_CPU_main(Input, Output, lambda, iter_numb, tau, dimX, dimY, dimZ);    
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/SB_TV.c b/src/Matlab/mex_compile/regularisers_CPU/SB_TV.c
new file mode 100644
index 0000000..8636322
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/SB_TV.c
@@ -0,0 +1,91 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "SB_TV_core.h"
+
+/* C-OMP implementation of Split Bregman - TV denoising-regularisation model (2D/3D) [1]
+*
+* Input Parameters:
+* 1. Noisy image/volume
+* 2. lambda - regularisation parameter
+* 3. Number of iterations [OPTIONAL parameter]
+* 4. eplsilon - tolerance constant [OPTIONAL parameter]
+* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
+* 6. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+*
+* Output:
+* 1. Filtered/regularized image
+*
+* This function is based on the Matlab's code and paper by
+* [1]. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.
+*/
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, printswitch;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    
+    float *Input, *Output=NULL, lambda, epsil;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), print switch");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 100; /* default iterations number */
+    epsil = 0.0001; /* default tolerance constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    printswitch = 0; /*default print is switched, off - 0 */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    if ((nrhs == 5) || (nrhs == 6))  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+        mxFree(penalty_type);
+    }
+    if (nrhs == 6)  {
+        printswitch = (int) mxGetScalar(prhs[5]);
+        if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0");
+    }
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */
+    SB_TV_CPU_main(Input, Output, lambda, iter, epsil, methTV, printswitch, dimX, dimY, dimZ);
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/TGV.c b/src/Matlab/mex_compile/regularisers_CPU/TGV.c
new file mode 100644
index 0000000..aa4eed4
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/TGV.c
@@ -0,0 +1,83 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "mex.h"
+#include "TGV_core.h"
+
+/* C-OMP implementation of Primal-Dual denoising method for 
+ * Total Generilized Variation (TGV)-L2 model [1] (2D/3D)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume (2D/3D)
+ * 2. lambda - regularisation parameter
+ * 3. parameter to control the first-order term (alpha1)
+ * 4. parameter to control the second-order term (alpha0)
+ * 5. Number of Chambolle-Pock (Primal-Dual) iterations
+ * 6. Lipshitz constant (default is 12)
+ *
+ * Output:
+ * Filtered/regulariaed image 
+ *
+ * References:
+ * [1] K. Bredies "Total Generalized Variation"
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    
+    float *Input, *Output=NULL, lambda, alpha0, alpha1, L2;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image/volume */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularisation parameter */
+    alpha1 =  1.0f; /* parameter to control the first-order term */ 
+    alpha0 =  0.5f; /* parameter to control the second-order term */
+    iter =  300; /* Iterations number */      
+    L2 =  12.0f; /* Lipshitz constant */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6))  alpha1 =  (float) mxGetScalar(prhs[2]); /* parameter to control the first-order term */ 
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6))  alpha0 =  (float) mxGetScalar(prhs[3]);  /* parameter to control the second-order term */
+    if ((nrhs == 5) || (nrhs == 6))  iter =  (int) mxGetScalar(prhs[4]); /* Iterations number */      
+    if (nrhs == 6)  L2 =  (float) mxGetScalar(prhs[5]); /* Lipshitz constant */
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));        
+    }
+    if (number_of_dims == 3) {
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    }       
+    /* running the function */
+    TGV_main(Input, Output, lambda, alpha1, alpha0, iter, L2, dimX, dimY, dimZ);        
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/TNV.c b/src/Matlab/mex_compile/regularisers_CPU/TNV.c
new file mode 100644
index 0000000..acea75d
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/TNV.c
@@ -0,0 +1,74 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "TNV_core.h"
+/*
+ * C-OMP implementation of Total Nuclear Variation regularisation model (2D + channels) [1]
+ * The code is modified from the implementation by Joan Duran <joan.duran@uib.es> see
+ * "denoisingPDHG_ipol.cpp" in Joans Collaborative Total Variation package
+ *
+ * Input Parameters:
+ * 1. Noisy volume of 2D + channel dimension, i.e. 3D volume
+ * 2. lambda - regularisation parameter
+ * 3. Number of iterations [OPTIONAL parameter]
+ * 4. eplsilon - tolerance constant [OPTIONAL parameter]
+ * 5. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+ *
+ * Output:
+ * 1. Filtered/regularized image
+ *
+ * [1]. Duran, J., Moeller, M., Sbert, C. and Cremers, D., 2016. Collaborative total variation: a general framework for vectorial TV models. SIAM Journal on Imaging Sciences, 9(1), pp.116-151.
+ */
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    float *Input, *Output=NULL, lambda, epsil;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 4)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D + channels), Regularisation parameter, Regularization parameter, iterations number, tolerance");
+    
+    Input  = (float *) mxGetData(prhs[0]); /* noisy sequence of channels (2D + channels) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 1000; /* default iterations number */
+    epsil = 1.00e-05; /* default tolerance constant */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if (nrhs == 4)  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) mexErrMsgTxt("The input must be 3D: [X,Y,Channels]");
+    if (number_of_dims == 3) {
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+        /* running the function */
+        TNV_CPU_main(Input, Output, lambda, iter, epsil, dimX, dimY, dimZ);
+    }
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_CPU/TV_energy.c b/src/Matlab/mex_compile/regularisers_CPU/TV_energy.c
new file mode 100644
index 0000000..d457f46
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_CPU/TV_energy.c
@@ -0,0 +1,72 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "utils.h"
+/*
+ * Function to calculate TV energy value with respect to the denoising variational problem
+ * 
+ * Input:
+ * 1. Denoised Image/volume
+ * 2. Original (noisy) Image/volume
+ * 3. lambda - regularisation parameter 
+ * 
+ * Output:
+ * 1. Energy function value
+ * 
+ */
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, type;
+    
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    float *Input, *Input0, lambda;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs != 4)) mexErrMsgTxt("4 inputs: Two images or volumes of the same size required, estimated and the original (noisy), regularisation parameter, type");
+    
+    Input  = (float *) mxGetData(prhs[0]); /* Denoised Image/volume */
+    Input0  = (float *) mxGetData(prhs[1]); /* Original (noisy) Image/volume */
+    lambda =  (float) mxGetScalar(prhs[2]); /* regularisation parameter */
+    type =  (int) mxGetScalar(prhs[3]); /* type of energy */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if (mxGetClassID(prhs[1]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    /*output energy function value */
+    plhs[0] = mxCreateNumericMatrix(1, 1, mxSINGLE_CLASS, mxREAL);
+    float *funcvalA = (float *) mxGetData(plhs[0]);
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) {
+		TV_energy2D(Input, Input0, funcvalA, lambda, type, dimX, dimY);
+		}
+    if (number_of_dims == 3) {
+        TV_energy3D(Input, Input0, funcvalA, lambda, type, dimX, dimY, dimZ);
+    }
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp
new file mode 100644
index 0000000..0cc042b
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp
@@ -0,0 +1,77 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "Diffus_4thO_GPU_core.h"
+
+/* CUDA implementation of fourth-order diffusion scheme [1] for piecewise-smooth recovery (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED]
+ * 3. Edge-preserving parameter (sigma) [REQUIRED]
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended [OPTIONAL, default 300]
+ * 5. tau - time-marching step for the explicit scheme [OPTIONAL, default 0.015]
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Hajiaboli, M.R., 2011. An anisotropic fourth-order diffusion filter for image noise removal. International Journal of Computer Vision, 92(2), pp.177-191.
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter_numb;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    float *Input, *Output=NULL, lambda, tau, sigma;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    sigma = (float) mxGetScalar(prhs[2]); /* Edge-preserving parameter */
+    iter_numb = 300; /* iterations number */
+    tau = 0.01; /* marching step parameter */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant");
+    if ((nrhs == 4) || (nrhs == 5))  iter_numb = (int) mxGetScalar(prhs[3]); /* iterations number */
+    if (nrhs == 5)  tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    Diffus4th_GPU_main(Input, Output, lambda, sigma, iter_numb, tau, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
new file mode 100644
index 0000000..c174e75
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
@@ -0,0 +1,97 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "TV_FGP_GPU_core.h"
+
+/* GPU (CUDA) implementation of FGP-TV [1] denoising/regularization model (2D/3D case)
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume
+ * 2. lambdaPar - regularization parameter
+ * 3. Number of iterations
+ * 4. eplsilon: tolerance constant
+ * 5. TV-type: methodTV - 'iso' (0) or 'l1' (1)
+ * 6. nonneg: 'nonnegativity (0 is OFF by default)
+ * 7. print information: 0 (off) or 1 (on)
+ *
+ * Output:
+ * [1] Filtered/regularized image
+ *
+ * This function is based on the Matlab's code and paper by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, printswitch, nonneg;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    
+    float *Input, *Output=NULL, lambda, epsil;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter. The full list of parameters: Image(2D/3D), Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), nonnegativity switch, print switch");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 300; /* default iterations number */
+    epsil = 0.0001; /* default tolerance constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    nonneg = 0; /* default nonnegativity switch, off - 0 */
+    printswitch = 0; /*default print is switched, off - 0 */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7))  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+        mxFree(penalty_type);
+    }
+    if ((nrhs == 6) || (nrhs == 7))  {
+        nonneg = (int) mxGetScalar(prhs[5]);
+        if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
+    }
+    if (nrhs == 7)  {
+        printswitch = (int) mxGetScalar(prhs[6]);
+        if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0");
+    }
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */
+    TV_FGP_GPU_main(Input, Output, lambda, iter, epsil, methTV, nonneg, printswitch, dimX, dimY, dimZ);    
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp
new file mode 100644
index 0000000..3f5a4b3
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp
@@ -0,0 +1,113 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "dTV_FGP_GPU_core.h"
+
+/* CUDA implementation of FGP-dTV [1,2] denoising/regularization model (2D/3D case)
+ * which employs structural similarity of the level sets of two images/volumes, see [1,2]
+ * The current implementation updates image 1 while image 2 is being fixed.
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. Additional reference image/volume of the same dimensions as (1) [REQUIRED]
+ * 3. lambdaPar - regularization parameter [REQUIRED]
+ * 4. Number of iterations [OPTIONAL]
+ * 5. eplsilon: tolerance constant [OPTIONAL]
+ * 6. eta: smoothing constant to calculate gradient of the reference [OPTIONAL] * 
+ * 7. TV-type: methodTV - 'iso' (0) or 'l1' (1) [OPTIONAL]
+ * 8. nonneg: 'nonnegativity (0 is OFF by default) [OPTIONAL]
+ * 9. print information: 0 (off) or 1 (on) [OPTIONAL]
+ *
+ * Output:
+ * [1] Filtered/regularized image/volume
+ *
+ * This function is based on the Matlab's codes and papers by
+ * [1] Amir Beck and Marc Teboulle, "Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems"
+ * [2] M. J. Ehrhardt and M. M. Betcke, Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation, SIAM Journal on Imaging Sciences 9(3), pp. 1084–1106
+ */
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, printswitch, nonneg;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    const mwSize *dim_array2;
+    
+    float *Input, *InputRef, *Output=NULL, lambda, epsil, eta;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    dim_array2 = mxGetDimensions(prhs[1]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 3) || (nrhs > 9)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Reference(2D/3D), Regularization parameter, iterations number, tolerance, smoothing constant, penalty type ('iso' or 'l1'), nonnegativity switch, print switch");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    InputRef  = (float *) mxGetData(prhs[1]); /* reference image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[2]); /* regularization parameter */
+    iter = 300; /* default iterations number */
+    epsil = 0.0001; /* default tolerance constant */
+    eta = 0.01; /* default smoothing constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    nonneg = 0; /* default nonnegativity switch, off - 0 */
+    printswitch = 0; /*default print is switched, off - 0 */
+    
+        
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if (mxGetClassID(prhs[1]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    if (number_of_dims == 2) { if ((dimX != dim_array2[0]) || (dimY != dim_array2[1])) mexErrMsgTxt("The input images have different dimensionalities");}
+    if (number_of_dims == 3) { if ((dimX != dim_array2[0]) || (dimY != dim_array2[1]) || (dimZ != dim_array2[2])) mexErrMsgTxt("The input volumes have different dimensionalities");}   
+    
+    
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8) || (nrhs == 9))  iter = (int) mxGetScalar(prhs[3]); /* iterations number */
+    if ((nrhs == 5) || (nrhs == 6) || (nrhs == 7) || (nrhs == 8) || (nrhs == 9))  epsil =  (float) mxGetScalar(prhs[4]); /* tolerance constant */
+    if ((nrhs == 6) || (nrhs == 7) || (nrhs == 8) || (nrhs == 9))  {
+    eta =  (float) mxGetScalar(prhs[5]); /* smoothing constant for the gradient of InputRef */
+    }
+    if ((nrhs == 7) || (nrhs == 8) || (nrhs == 9))  {        
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[6]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+        mxFree(penalty_type);
+    }    
+    if ((nrhs == 8) || (nrhs == 9))  {
+        nonneg = (int) mxGetScalar(prhs[7]);
+        if ((nonneg != 0) && (nonneg != 1)) mexErrMsgTxt("Nonnegativity constraint can be enabled by choosing 1 or off - 0");
+    }
+    if (nrhs == 9)  {
+        printswitch = (int) mxGetScalar(prhs[8]);
+        if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0");
+    }    
+   
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */
+    dTV_FGP_GPU_main(Input, InputRef, Output, lambda, iter, epsil, eta, methTV, nonneg, printswitch, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
new file mode 100644
index 0000000..e8da4ce
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
@@ -0,0 +1,83 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "LLT_ROF_GPU_core.h"
+
+/* CUDA implementation of Lysaker, Lundervold and Tai (LLT) model [1] combined with Rudin-Osher-Fatemi [2] TV regularisation penalty.
+* 
+* This penalty can deliver visually pleasant piecewise-smooth recovery if regularisation parameters are selected well. 
+* The rule of thumb for selection is to start with lambdaLLT = 0 (just the ROF-TV model) and then proceed to increase 
+* lambdaLLT starting with smaller values. 
+*
+* Input Parameters:
+* 1. U0 - original noise image/volume
+* 2. lambdaROF - ROF-related regularisation parameter
+* 3. lambdaLLT - LLT-related regularisation parameter
+* 4. tau - time-marching step 
+* 5. iter - iterations number (for both models)
+*
+* Output:
+* Filtered/regularised image
+*
+* References: 
+* [1] Lysaker, M., Lundervold, A. and Tai, X.C., 2003. Noise removal using fourth-order partial differential equation with applications to medical magnetic resonance images in space and time. IEEE Transactions on image processing, 12(12), pp.1579-1590.
+* [2] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+*/
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iterationsNumb;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    
+    float *Input, *Output=NULL, lambdaROF, lambdaLLT, tau;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    if ((nrhs < 3) || (nrhs > 5)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter (ROF), Regularisation parameter (LTT), iterations number, time-marching parameter");
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambdaROF =  (float) mxGetScalar(prhs[1]); /* ROF regularization parameter */
+    lambdaLLT =  (float) mxGetScalar(prhs[2]); /* ROF regularization parameter */    
+    iterationsNumb = 250;
+    tau =  0.0025;
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
+    if ((nrhs == 4) || (nrhs == 5)) iterationsNumb =  (int) mxGetScalar(prhs[3]); /* iterations number */    
+    if (nrhs == 5) tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */  
+        
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));                        
+    }    
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));   
+  
+    LLT_ROF_GPU_main(Input, Output, lambdaROF, lambdaLLT, iterationsNumb, tau, dimX, dimY, dimZ);    
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp
new file mode 100644
index 0000000..1cd0cdc
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp
@@ -0,0 +1,92 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include <stdio.h>
+#include <string.h>
+#include "NonlDiff_GPU_core.h"
+
+/* CUDA implementation of linear and nonlinear diffusion with the regularisation model [1,2] (2D/3D case)
+ * The minimisation is performed using explicit scheme. 
+ *
+ * Input Parameters:
+ * 1. Noisy image/volume 
+ * 2. lambda - regularization parameter
+ * 3. Edge-preserving parameter (sigma), when sigma equals to zero nonlinear diffusion -> linear diffusion
+ * 4. Number of iterations, for explicit scheme >= 150 is recommended 
+ * 5. tau - time-marching step for explicit scheme
+ * 6. Penalty type: 1 - Huber, 2 - Perona-Malik, 3 - Tukey Biweight
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Perona, P. and Malik, J., 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on pattern analysis and machine intelligence, 12(7), pp.629-639.
+ * [2] Black, M.J., Sapiro, G., Marimont, D.H. and Heeger, D., 1998. Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), pp.421-432.
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter_numb, penaltytype;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    
+    float *Input, *Output=NULL, lambda, tau, sigma;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    sigma = (float) mxGetScalar(prhs[2]); /* Edge-preserving parameter */
+    iter_numb = 300; /* iterations number */
+    tau = 0.025; /* marching step parameter */
+    penaltytype = 1; /* Huber penalty by default */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if ((nrhs < 3) || (nrhs > 6)) mexErrMsgTxt("At least 3 parameters is required, all parameters are: Image(2D/3D), Regularisation parameter, Edge-preserving parameter, iterations number, time-marching constant, penalty type - Huber, PM or Tukey");
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6))  iter_numb = (int) mxGetScalar(prhs[3]); /* iterations number */
+    if ((nrhs == 5) || (nrhs == 6))  tau =  (float) mxGetScalar(prhs[4]); /* marching step parameter */
+    if (nrhs == 6)  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[5]); /* Huber, PM or Tukey 'Huber' is the default */
+        if ((strcmp(penalty_type, "Huber") != 0) && (strcmp(penalty_type, "PM") != 0) && (strcmp(penalty_type, "Tukey") != 0)) mexErrMsgTxt("Choose penalty: 'Huber', 'PM' or 'Tukey',");
+        if (strcmp(penalty_type, "Huber") == 0)  penaltytype = 1;  /* enable 'Huber' penalty */
+        if (strcmp(penalty_type, "PM") == 0)  penaltytype = 2;  /* enable Perona-Malik penalty */
+        if (strcmp(penalty_type, "Tukey") == 0)  penaltytype = 3;  /* enable Tikey Biweight penalty */
+        mxFree(penalty_type);
+    }    
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    NonlDiff_GPU_main(Input, Output, lambda, sigma, iter_numb, tau, penaltytype, dimX, dimY, dimZ);
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
new file mode 100644
index 0000000..bd01d55
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
@@ -0,0 +1,74 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "TV_ROF_GPU_core.h"
+
+/* ROF-TV denoising/regularization model [1] (2D/3D case)
+ * (MEX wrapper for MATLAB)
+ * 
+ * Input Parameters:
+ * 1. Noisy image/volume [REQUIRED]
+ * 2. lambda - regularization parameter [REQUIRED]
+ * 3. Number of iterations, for explicit scheme >= 150 is recommended  [REQUIRED]
+ * 4. tau - marching step for explicit scheme, ~1 is recommended [REQUIRED]
+ *
+ * Output:
+ * [1] Regularized image/volume 
+ *
+ * This function is based on the paper by
+ * [1] Rudin, Osher, Fatemi, "Nonlinear Total Variation based noise removal algorithms"
+ *
+ * D. Kazantsev, 2016-18
+ */
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter_numb;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    
+    float *Input, *Output=NULL, lambda, tau;
+    
+    dim_array = mxGetDimensions(prhs[0]);
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    Input  = (float *) mxGetData(prhs[0]);
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter_numb =  (int) mxGetScalar(prhs[2]); /* iterations number */
+    tau =  (float) mxGetScalar(prhs[3]); /* marching step parameter */  
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    if(nrhs != 4) mexErrMsgTxt("Four inputs reqired: Image(2D,3D), regularization parameter, iterations number,  marching step constant");
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    /* output arrays*/
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        /* output image/volume */
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));                        
+    }    
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    TV_ROF_GPU_main(Input, Output, lambda, iter_numb, tau, dimX, dimY, dimZ);    
+}
\ No newline at end of file
diff --git a/src/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp
new file mode 100644
index 0000000..9d1328f
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp
@@ -0,0 +1,91 @@
+/*
+ * This work is part of the Core Imaging Library developed by
+ * Visual Analytics and Imaging System Group of the Science Technology
+ * Facilities Council, STFC
+ *
+ * Copyright 2017 Daniil Kazantsev
+ * Copyright 2017 Srikanth Nagella, Edoardo Pasca
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ * http://www.apache.org/licenses/LICENSE-2.0
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "matrix.h"
+#include "mex.h"
+#include "TV_SB_GPU_core.h"
+
+/* CUDA mex-file for implementation of Split Bregman - TV denoising-regularisation model (2D/3D) [1]
+*
+* Input Parameters:
+* 1. Noisy image/volume
+* 2. lambda - regularisation parameter
+* 3. Number of iterations [OPTIONAL parameter]
+* 4. eplsilon - tolerance constant [OPTIONAL parameter]
+* 5. TV-type: 'iso' or 'l1' [OPTIONAL parameter]
+* 6. print information: 0 (off) or 1 (on)  [OPTIONAL parameter]
+*
+* Output:
+* 1. Filtered/regularized image
+*
+* This function is based on the Matlab's code and paper by
+* [1]. Goldstein, T. and Osher, S., 2009. The split Bregman method for L1-regularized problems. SIAM journal on imaging sciences, 2(2), pp.323-343.
+*/
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter, methTV, printswitch;
+    mwSize dimX, dimY, dimZ;
+    const mwSize *dim_array;
+    
+    float *Input, *Output=NULL, lambda, epsil;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D/3D), Regularization parameter, Regularization parameter, iterations number, tolerance, penalty type ('iso' or 'l1'), print switch");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D/3D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularization parameter */
+    iter = 100; /* default iterations number */
+    epsil = 0.0001; /* default tolerance constant */
+    methTV = 0;  /* default isotropic TV penalty */
+    printswitch = 0; /*default print is switched, off - 0 */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }
+    
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6))  iter = (int) mxGetScalar(prhs[2]); /* iterations number */
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6))  epsil =  (float) mxGetScalar(prhs[3]); /* tolerance constant */
+    if ((nrhs == 5) || (nrhs == 6))  {
+        char *penalty_type;
+        penalty_type = mxArrayToString(prhs[4]); /* choosing TV penalty: 'iso' or 'l1', 'iso' is the default */
+        if ((strcmp(penalty_type, "l1") != 0) && (strcmp(penalty_type, "iso") != 0)) mexErrMsgTxt("Choose TV type: 'iso' or 'l1',");
+        if (strcmp(penalty_type, "l1") == 0)  methTV = 1;  /* enable 'l1' penalty */
+        mxFree(penalty_type);
+    }
+    if (nrhs == 6)  {
+        printswitch = (int) mxGetScalar(prhs[5]);
+        if ((printswitch != 0) && (printswitch != 1)) mexErrMsgTxt("Print can be enabled by choosing 1 or off - 0");
+    }
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1]; dimZ = dim_array[2];
+    
+    if (number_of_dims == 2) {
+        dimZ = 1; /*2D case*/
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+    }
+    if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
+    
+    /* running the function */
+    TV_SB_GPU_main(Input, Output, lambda, iter, epsil, methTV, printswitch, dimX, dimY, dimZ);
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
new file mode 100644
index 0000000..edb551d
--- /dev/null
+++ b/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
@@ -0,0 +1,79 @@
+/*
+This work is part of the Core Imaging Library developed by
+Visual Analytics and Imaging System Group of the Science Technology
+Facilities Council, STFC
+
+Copyright 2017 Daniil Kazantsev
+Copyright 2017 Srikanth Nagella, Edoardo Pasca
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+*/
+
+#include "mex.h"
+#include "TGV_GPU_core.h"
+
+/* CUDA implementation of Primal-Dual denoising method for 
+ * Total Generilized Variation (TGV)-L2 model [1] (2D case only)
+ *
+ * Input Parameters:
+ * 1. Noisy image (2D) (required)
+ * 2. lambda - regularisation parameter (required)
+ * 3. parameter to control the first-order term (alpha1) (default - 1)
+ * 4. parameter to control the second-order term (alpha0) (default - 0.5)
+ * 5. Number of Chambolle-Pock (Primal-Dual) iterations (default is 300)
+ * 6. Lipshitz constant (default is 12)
+ *
+ * Output:
+ * Filtered/regulariaed image 
+ *
+ * References:
+ * [1] K. Bredies "Total Generalized Variation"
+ */
+
+void mexFunction(
+        int nlhs, mxArray *plhs[],
+        int nrhs, const mxArray *prhs[])
+        
+{
+    int number_of_dims, iter;
+    mwSize dimX, dimY;
+    const mwSize *dim_array;
+    float *Input, *Output=NULL, lambda, alpha0, alpha1, L2;
+    
+    number_of_dims = mxGetNumberOfDimensions(prhs[0]);
+    dim_array = mxGetDimensions(prhs[0]);
+    
+    /*Handling Matlab input data*/
+    if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
+    
+    Input  = (float *) mxGetData(prhs[0]); /*noisy image (2D) */
+    lambda =  (float) mxGetScalar(prhs[1]); /* regularisation parameter */
+    alpha1 =  1.0f; /* parameter to control the first-order term */ 
+    alpha0 =  0.5f; /* parameter to control the second-order term */
+    iter =  300; /* Iterations number */      
+    L2 =  12.0f; /* Lipshitz constant */
+    
+    if (mxGetClassID(prhs[0]) != mxSINGLE_CLASS) {mexErrMsgTxt("The input image must be in a single precision"); }   
+    if ((nrhs == 3) || (nrhs == 4) || (nrhs == 5) || (nrhs == 6))  alpha1 =  (float) mxGetScalar(prhs[2]); /* parameter to control the first-order term */ 
+    if ((nrhs == 4) || (nrhs == 5) || (nrhs == 6))  alpha0 =  (float) mxGetScalar(prhs[3]);  /* parameter to control the second-order term */
+    if ((nrhs == 5) || (nrhs == 6))  iter =  (int) mxGetScalar(prhs[4]); /* Iterations number */      
+    if (nrhs == 6)  L2 =  (float) mxGetScalar(prhs[5]); /* Lipshitz constant */
+    
+    /*Handling Matlab output data*/
+    dimX = dim_array[0]; dimY = dim_array[1];
+    
+    if (number_of_dims == 2) {
+        Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(2, dim_array, mxSINGLE_CLASS, mxREAL));
+        /* running the function */
+        TGV_GPU_main(Input, Output, lambda, alpha1, alpha0, iter, L2, dimX, dimY);        
+    }
+    if (number_of_dims == 3) {mexErrMsgTxt("Only 2D images accepted");}       
+}
diff --git a/src/Matlab/supp/RMSE.m b/src/Matlab/supp/RMSE.m
new file mode 100644
index 0000000..002f776
--- /dev/null
+++ b/src/Matlab/supp/RMSE.m
@@ -0,0 +1,7 @@
+function err = RMSE(signal1, signal2)

+%RMSE Root Mean Squared Error

+

+err = sum((signal1 - signal2).^2)/length(signal1);  % MSE

+err = sqrt(err);                                    % RMSE

+

+end
\ No newline at end of file
diff --git a/src/Matlab/supp/my_red_yellowMAP.mat b/src/Matlab/supp/my_red_yellowMAP.mat
new file mode 100644
index 0000000..c2a5b87
--- /dev/null
+++ b/src/Matlab/supp/my_red_yellowMAP.mat
diff --git a/src/Python/CMakeLists.txt b/src/Python/CMakeLists.txt
new file mode 100644
index 0000000..c2ef855
--- /dev/null
+++ b/src/Python/CMakeLists.txt
@@ -0,0 +1,141 @@
+#   Copyright 2018 Edoardo Pasca
+cmake_minimum_required (VERSION 3.0)
+
+project(regulariserPython)
+#https://stackoverflow.com/questions/13298504/using-cmake-with-setup-py
+
+# The version number.
+
+#set (CIL_VERSION $ENV{CIL_VERSION} CACHE INTERNAL "Core Imaging Library version" FORCE)
+
+# conda orchestrated build
+message("CIL_VERSION: ${CIL_VERSION}")
+#include (GenerateExportHeader)
+
+find_package(PythonInterp REQUIRED)
+if (PYTHONINTERP_FOUND)
+  message ("Current Python " ${PYTHON_VERSION_STRING} " found " ${PYTHON_EXECUTABLE})
+endif()
+
+	
+## Build the regularisers package as a library
+message("Creating Regularisers as shared library")
+
+message("CMAKE_CXX_FLAGS ${CMAKE_CXX_FLAGS}")
+
+set(CMAKE_BUILD_TYPE "Release")
+
+if(WIN32)
+  set (FLAGS "/DWIN32 /EHsc /openmp /DCCPiCore_EXPORTS")
+  set (CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} /NODEFAULTLIB:MSVCRT.lib")
+  
+  set (EXTRA_LIBRARIES)
+		
+  message("library lib: ${LIBRARY_LIB}")
+  
+elseif(UNIX)
+   set (FLAGS "-fopenmp -O2 -funsigned-char -Wall  -Wl,--no-undefined  -DCCPiReconstructionIterative_EXPORTS -std=c++0x")  
+   set (EXTRA_LIBRARIES 
+		"gomp"
+		)
+endif()
+
+# GPU regularisers
+if (BUILD_CUDA)
+    find_package(CUDA)
+    if (CUDA_FOUND)
+      message("CUDA FOUND")
+      set (SETUP_GPU_WRAPPERS "extra_libraries += ['cilregcuda']\n\
+setup( \n\
+        name='ccpi', \n\
+        description='CCPi Core Imaging Library - Image regularisers GPU',\n\
+        version=cil_version,\n\
+        cmdclass = {'build_ext': build_ext},\n\
+        ext_modules = [Extension('ccpi.filters.gpu_regularisers',\n\
+                                  sources=[ \n\
+                                          os.path.join('.' , 'src', 'gpu_regularisers.pyx' ),\n\
+                                            ],\n\
+                                 include_dirs=extra_include_dirs, \n\
+                                 library_dirs=extra_library_dirs, \n\
+                                 extra_compile_args=extra_compile_args, \n\
+                                 libraries=extra_libraries ), \n\
+        ],\n\
+        zip_safe = False,	\n\
+        packages = {'ccpi','ccpi.filters'},\n\
+    )")
+    else()
+      message("CUDA NOT FOUND")
+      set(SETUP_GPU_WRAPPERS "#CUDA NOT FOUND")
+    endif()
+endif()
+configure_file("${CMAKE_CURRENT_SOURCE_DIR}/setup-regularisers.py.in" "${CMAKE_CURRENT_BINARY_DIR}/setup-regularisers.py")
+
+
+find_package(PythonInterp)
+find_package(PythonLibs)
+if (PYTHONINTERP_FOUND)
+  message(STATUS "Found PYTHON_EXECUTABLE=${PYTHON_EXECUTABLE}")
+  message(STATUS "Python version ${PYTHON_VERSION_STRING}")
+endif()
+if (PYTHONLIBS_FOUND)
+  message(STATUS "Found PYTHON_INCLUDE_DIRS=${PYTHON_INCLUDE_DIRS}")
+  message(STATUS "Found PYTHON_LIBRARIES=${PYTHON_LIBRARIES}")
+endif()
+
+if (PYTHONINTERP_FOUND)
+    message("Python found " ${PYTHON_EXECUTABLE})
+    set(SETUP_PY_IN "${CMAKE_CURRENT_SOURCE_DIR}/setup-regularisers.py.in")
+    set(SETUP_PY    "${CMAKE_CURRENT_BINARY_DIR}/setup-regularisers.py")
+    #set(DEPS        "${CMAKE_CURRENT_SOURCE_DIR}/module/__init__.py")
+    set (DEPS       "${CMAKE_BINARY_DIR}/Core/")
+    set(OUTPUT      "${CMAKE_CURRENT_BINARY_DIR}/build/timestamp")
+
+    configure_file(${SETUP_PY_IN} ${SETUP_PY})
+
+    message("Core binary dir " ${CMAKE_BINARY_DIR}/Core/${CMAKE_BUILD_TYPE})
+    
+    if (CONDA_BUILD)
+      add_custom_command(OUTPUT ${OUTPUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/src ${CMAKE_CURRENT_BINARY_DIR}/src
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/ccpi ${CMAKE_CURRENT_BINARY_DIR}/ccpi
+                       COMMAND ${CMAKE_COMMAND} -E env CIL_VERSION=${CIL_VERSION}
+                                                       PREFIX=${CMAKE_SOURCE_DIR}/Core 
+                                                       LIBRARY_INC=${CMAKE_SOURCE_DIR}/Core 
+                                                       LIBRARY_LIB=${CMAKE_BINARY_DIR}/Core
+                                                       ${PYTHON_EXECUTABLE} ${SETUP_PY} install
+                       COMMAND ${CMAKE_COMMAND} -E touch ${OUTPUT}
+                       DEPENDS cilreg)
+
+    else()
+      if (WIN32)
+        add_custom_command(OUTPUT ${OUTPUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/src ${CMAKE_CURRENT_BINARY_DIR}/src
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/ccpi ${CMAKE_CURRENT_BINARY_DIR}/ccpi
+                       COMMAND ${CMAKE_COMMAND} -E env CIL_VERSION=${CIL_VERSION}
+                                                       PREFIX=${CMAKE_SOURCE_DIR}/Core 
+                                                       LIBRARY_INC=${CMAKE_SOURCE_DIR}/Core 
+                                                       LIBRARY_LIB=${CMAKE_BINARY_DIR}/Core/${CMAKE_BUILD_TYPE}
+                                                       ${PYTHON_EXECUTABLE} ${SETUP_PY} build_ext --inplace
+                       COMMAND ${CMAKE_COMMAND} -E touch ${OUTPUT}
+                       DEPENDS cilreg)
+      else()
+        add_custom_command(OUTPUT ${OUTPUT}
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/src ${CMAKE_CURRENT_BINARY_DIR}/src
+                       COMMAND ${CMAKE_COMMAND} -E copy_directory ${CMAKE_CURRENT_SOURCE_DIR}/ccpi ${CMAKE_CURRENT_BINARY_DIR}/ccpi
+                       COMMAND ${CMAKE_COMMAND} -E env CIL_VERSION=${CIL_VERSION}
+                                                       PREFIX=${CMAKE_SOURCE_DIR}/Core 
+                                                       LIBRARY_INC=${CMAKE_SOURCE_DIR}/Core 
+                                                       LIBRARY_LIB=${CMAKE_BINARY_DIR}/Core
+                                                       ${PYTHON_EXECUTABLE} ${SETUP_PY} build_ext --inplace
+                       COMMAND ${CMAKE_COMMAND} -E touch ${OUTPUT}
+                       DEPENDS cilreg)
+      endif()
+      install(DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/ccpi 
+              DESTINATION ${PYTHON_DEST})
+    endif()
+    
+    
+    add_custom_target(PythonWrapper ALL DEPENDS ${OUTPUT})
+
+    #install(CODE "execute_process(COMMAND ${PYTHON} ${SETUP_PY} install)")
+endif()
diff --git a/src/Python/ccpi/__init__.py b/src/Python/ccpi/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/src/Python/ccpi/__init__.py
diff --git a/src/Python/ccpi/filters/__init__.py b/src/Python/ccpi/filters/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/src/Python/ccpi/filters/__init__.py
diff --git a/src/Python/ccpi/filters/regularisers.py b/src/Python/ccpi/filters/regularisers.py
new file mode 100644
index 0000000..588ea32
--- /dev/null
+++ b/src/Python/ccpi/filters/regularisers.py
@@ -0,0 +1,214 @@
+"""
+script which assigns a proper device core function based on a flag ('cpu' or 'gpu')
+"""
+
+from ccpi.filters.cpu_regularisers import TV_ROF_CPU, TV_FGP_CPU, TV_SB_CPU, dTV_FGP_CPU, TNV_CPU, NDF_CPU, Diff4th_CPU, TGV_CPU, LLT_ROF_CPU, PATCHSEL_CPU, NLTV_CPU
+try:
+    from ccpi.filters.gpu_regularisers import TV_ROF_GPU, TV_FGP_GPU, TV_SB_GPU, dTV_FGP_GPU, NDF_GPU, Diff4th_GPU, TGV_GPU, LLT_ROF_GPU, PATCHSEL_GPU
+    gpu_enabled = True
+except ImportError:
+    gpu_enabled = False    
+from ccpi.filters.cpu_regularisers import NDF_INPAINT_CPU, NVM_INPAINT_CPU
+
+def ROF_TV(inputData, regularisation_parameter, iterations,
+                     time_marching_parameter,device='cpu'):
+    if device == 'cpu':
+        return TV_ROF_CPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     time_marching_parameter)
+    elif device == 'gpu' and gpu_enabled:
+        return TV_ROF_GPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     time_marching_parameter)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+
+def FGP_TV(inputData, regularisation_parameter,iterations,
+                     tolerance_param, methodTV, nonneg, printM, device='cpu'):
+    if device == 'cpu':
+        return TV_FGP_CPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     nonneg,
+                     printM)
+    elif device == 'gpu' and gpu_enabled:
+        return TV_FGP_GPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     nonneg,
+                     printM)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+def SB_TV(inputData, regularisation_parameter, iterations,
+                     tolerance_param, methodTV, printM, device='cpu'):
+    if device == 'cpu':
+        return TV_SB_CPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     printM)
+    elif device == 'gpu' and gpu_enabled:
+        return TV_SB_GPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     printM)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+def FGP_dTV(inputData, refdata, regularisation_parameter, iterations,
+                     tolerance_param, eta_const, methodTV, nonneg, printM, device='cpu'):
+    if device == 'cpu':
+        return dTV_FGP_CPU(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
+    elif device == 'gpu' and gpu_enabled:
+        return dTV_FGP_GPU(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+def TNV(inputData, regularisation_parameter, iterations, tolerance_param):
+        return TNV_CPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param)
+def NDF(inputData, regularisation_parameter, edge_parameter, iterations,
+                     time_marching_parameter, penalty_type, device='cpu'):
+    if device == 'cpu':
+        return NDF_CPU(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter,
+                     penalty_type)
+    elif device == 'gpu' and gpu_enabled:
+        return NDF_GPU(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter,
+                     penalty_type)
+    else:
+        if not gpu_enabled and device == 'gpu':
+    	    raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+def Diff4th(inputData, regularisation_parameter, edge_parameter, iterations,
+                     time_marching_parameter, device='cpu'):
+    if device == 'cpu':
+        return Diff4th_CPU(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter)
+    elif device == 'gpu' and gpu_enabled:
+        return Diff4th_GPU(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+        
+def PatchSelect(inputData, searchwindow, patchwindow, neighbours, edge_parameter, device='cpu'):
+    if device == 'cpu':
+        return PATCHSEL_CPU(inputData,
+                     searchwindow,
+                     patchwindow,
+                     neighbours, 
+                     edge_parameter)
+    elif device == 'gpu' and gpu_enabled:
+        return PATCHSEL_GPU(inputData,
+                     searchwindow,
+                     patchwindow,
+                     neighbours, 
+                     edge_parameter)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+
+def NLTV(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations):
+    return NLTV_CPU(inputData,
+                     H_i,
+                     H_j,
+                     H_k, 
+                     Weights,
+                     regularisation_parameter,
+                     iterations)
+
+def TGV(inputData, regularisation_parameter, alpha1, alpha0, iterations,
+                     LipshitzConst, device='cpu'):
+    if device == 'cpu':
+        return TGV_CPU(inputData, 
+					regularisation_parameter, 
+					alpha1, 
+					alpha0, 
+					iterations,
+                    LipshitzConst)
+    elif device == 'gpu' and gpu_enabled:
+        return TGV_GPU(inputData, 
+					regularisation_parameter, 
+					alpha1, 
+					alpha0, 
+					iterations,
+                    LipshitzConst)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+def LLT_ROF(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations,
+                     time_marching_parameter, device='cpu'):
+    if device == 'cpu':
+        return LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif device == 'gpu' and gpu_enabled:
+        return LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    else:
+        if not gpu_enabled and device == 'gpu':
+            raise ValueError ('GPU is not available')
+        raise ValueError('Unknown device {0}. Expecting gpu or cpu'\
+                         .format(device))
+def NDF_INP(inputData, maskData, regularisation_parameter, edge_parameter, iterations,
+                     time_marching_parameter, penalty_type):
+        return NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, 
+        edge_parameter, iterations, time_marching_parameter, penalty_type)
+        
+def NVM_INP(inputData, maskData, SW_increment, iterations):
+        return NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterations)
diff --git a/src/Python/setup-regularisers.py.in b/src/Python/setup-regularisers.py.in
new file mode 100644
index 0000000..462edda
--- /dev/null
+++ b/src/Python/setup-regularisers.py.in
@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+
+import setuptools
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+import os
+import sys
+import numpy
+import platform	
+
+cil_version=os.environ['CIL_VERSION']
+if  cil_version == '':
+    print("Please set the environmental variable CIL_VERSION")
+    sys.exit(1)
+	
+library_include_path = ""
+library_lib_path = ""
+try:
+    library_include_path = os.environ['LIBRARY_INC']
+    library_lib_path = os.environ['LIBRARY_LIB']
+except:
+    library_include_path = os.environ['PREFIX']+'/include'
+    pass
+    
+extra_include_dirs = [numpy.get_include(), library_include_path]
+#extra_library_dirs = [os.path.join(library_include_path, "..", "lib")]
+extra_compile_args = []
+extra_library_dirs = [library_lib_path]
+extra_compile_args = []
+extra_link_args = []
+extra_libraries = ['cilreg']
+
+print ("extra_library_dirs " , extra_library_dirs)
+
+extra_include_dirs += [os.path.join(".." , ".." , "Core"),
+                       os.path.join(".." , ".." , "Core",  "regularisers_CPU"),
+                       os.path.join(".." , ".." , "Core",  "inpainters_CPU"),
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_FGP" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_ROF" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TV_SB" ) ,
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "TGV" ) ,
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "LLTROF" ) ,
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "NDF" ) ,
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "dTV_FGP" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "DIFF4th" ) , 
+                       os.path.join(".." , ".." , "Core",  "regularisers_GPU" , "PatchSelect" ) ,
+						   "."]
+
+if platform.system() == 'Windows':				   
+    extra_compile_args[0:] = ['/DWIN32','/EHsc','/DBOOST_ALL_NO_LIB' , '/openmp' ]   
+else:
+    extra_compile_args = ['-fopenmp','-O2', '-funsigned-char', '-Wall', '-std=c++0x']
+    extra_libraries += [@EXTRA_OMP_LIB@]
+    
+setup(
+    name='ccpi',
+	description='CCPi Core Imaging Library - Image regularisers',
+	version=cil_version,
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [Extension("ccpi.filters.cpu_regularisers",
+                             sources=[os.path.join("." , "src", "cpu_regularisers.pyx" ) ],
+                             include_dirs=extra_include_dirs, 
+							 library_dirs=extra_library_dirs, 
+							 extra_compile_args=extra_compile_args, 
+							 libraries=extra_libraries ), 
+    
+    ],
+	zip_safe = False,	
+	packages = {'ccpi','ccpi.filters'},
+)
+
+
+@SETUP_GPU_WRAPPERS@
diff --git a/src/Python/src/cpu_regularisers.pyx b/src/Python/src/cpu_regularisers.pyx
new file mode 100644
index 0000000..11a0617
--- /dev/null
+++ b/src/Python/src/cpu_regularisers.pyx
@@ -0,0 +1,685 @@
+# distutils: language=c++
+"""
+Copyright 2018 CCPi
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Author: Edoardo Pasca, Daniil Kazantsev
+"""
+
+import cython
+import numpy as np
+cimport numpy as np
+
+cdef extern float TV_ROF_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
+cdef extern float TV_FGP_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+cdef extern float SB_TV_CPU_main(float *Input, float *Output, float lambdaPar, int iterationsNumb, float epsil, int methodTV, int printM, int dimX, int dimY, int dimZ);
+cdef extern float LLT_ROF_CPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
+cdef extern float TGV_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ);
+cdef extern float Diffusion_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
+cdef extern float Diffus4th_CPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int dimX, int dimY, int dimZ);
+cdef extern float TNV_CPU_main(float *Input, float *u, float lambdaPar, int maxIter, float tol, int dimX, int dimY, int dimZ);
+cdef extern float dTV_FGP_CPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int dimX, int dimY, int dimZ);
+cdef extern float PatchSelect_CPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int SearchWindow, int SimilarWin, int NumNeighb, float h, int switchM);
+cdef extern float Nonlocal_TV_CPU_main(float *A_orig, float *Output, unsigned short *H_i, unsigned short *H_j, unsigned short *H_k, float *Weights, int dimX, int dimY, int dimZ, int NumNeighb, float lambdaReg, int IterNumb);
+
+cdef extern float Diffusion_Inpaint_CPU_main(float *Input, unsigned char *Mask, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int dimX, int dimY, int dimZ);
+cdef extern float NonlocalMarching_Inpaint_main(float *Input, unsigned char *M, float *Output, unsigned char *M_upd, int SW_increment, int iterationsNumb, int trigger, int dimX, int dimY, int dimZ);
+cdef extern float TV_energy2D(float *U, float *U0, float *E_val, float lambdaPar, int type, int dimX, int dimY);
+cdef extern float TV_energy3D(float *U, float *U0, float *E_val, float lambdaPar, int type, int dimX, int dimY, int dimZ);
+#****************************************************************#
+#********************** Total-variation ROF *********************#
+#****************************************************************#
+def TV_ROF_CPU(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter):
+    if inputData.ndim == 2:
+        return TV_ROF_2D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter)
+    elif inputData.ndim == 3:
+        return TV_ROF_3D(inputData, regularisation_parameter, iterationsNumb, marching_step_parameter)
+
+def TV_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb,                     
+                     float marching_step_parameter):
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    # Run ROF iterations for 2D data 
+    TV_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[1], dims[0], 1)
+    
+    return outputData
+            
+def TV_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb,
+                     float marching_step_parameter):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+           
+    # Run ROF iterations for 3D data 
+    TV_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, marching_step_parameter, dims[2], dims[1], dims[0])
+
+    return outputData
+
+#****************************************************************#
+#********************** Total-variation FGP *********************#
+#****************************************************************#
+#******** Total-variation Fast-Gradient-Projection (FGP)*********#
+def TV_FGP_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM):
+    if inputData.ndim == 2:
+        return TV_FGP_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
+    elif inputData.ndim == 3:
+        return TV_FGP_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, nonneg, printM)
+
+def TV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run FGP-TV iterations for 2D data */
+    TV_FGP_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, 
+                       iterationsNumb, 
+                       tolerance_param,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[1],dims[0],1)
+    
+    return outputData        
+            
+def TV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+           
+    #/* Run FGP-TV iterations for 3D data */
+    TV_FGP_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
+                       iterationsNumb, 
+                       tolerance_param,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[2], dims[1], dims[0])
+    return outputData 
+
+#***************************************************************#
+#********************** Total-variation SB *********************#
+#***************************************************************#
+#*************** Total-variation Split Bregman (SB)*************#
+def TV_SB_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM):
+    if inputData.ndim == 2:
+        return TV_SB_2D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM)
+    elif inputData.ndim == 3:
+        return TV_SB_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param, methodTV, printM)
+
+def TV_SB_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     int methodTV,
+                     int printM):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run SB-TV iterations for 2D data */
+    SB_TV_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, 
+                       iterationsNumb, 
+                       tolerance_param,
+                       methodTV,
+                       printM,
+                       dims[1],dims[0],1)
+    
+    return outputData        
+            
+def TV_SB_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     int methodTV,
+                     int printM):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+           
+    #/* Run SB-TV iterations for 3D data */
+    SB_TV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
+                       iterationsNumb, 
+                       tolerance_param,
+                       methodTV,
+                       printM,
+                       dims[2], dims[1], dims[0])
+    return outputData 
+
+#***************************************************************#
+#***************** Total Generalised Variation *****************#
+#***************************************************************#
+def TGV_CPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
+    if inputData.ndim == 2:
+        return TGV_2D(inputData, regularisation_parameter, alpha1, alpha0, 
+                      iterations, LipshitzConst)
+    elif inputData.ndim == 3:
+        return TGV_3D(inputData, regularisation_parameter, alpha1, alpha0, 
+                      iterations, LipshitzConst)
+
+def TGV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run TGV iterations for 2D data */
+    TGV_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, 
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[1],dims[0],1)
+    return outputData
+def TGV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+                         
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+                   
+    #/* Run TGV iterations for 3D data */
+    TGV_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, 
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[2], dims[1], dims[0])
+    return outputData
+
+#***************************************************************#
+#******************* ROF - LLT regularisation ******************#
+#***************************************************************#
+def LLT_ROF_CPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
+    if inputData.ndim == 2:
+        return LLT_ROF_2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif inputData.ndim == 3:
+        return LLT_ROF_3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+
+def LLT_ROF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run ROF-LLT iterations for 2D data */
+    LLT_ROF_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1)
+    return outputData
+
+def LLT_ROF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+						 
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+           
+    #/* Run ROF-LLT iterations for 3D data */
+    LLT_ROF_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0])
+    return outputData 
+
+#****************************************************************#
+#**************Directional Total-variation FGP ******************#
+#****************************************************************#
+#******** Directional TV Fast-Gradient-Projection (FGP)*********#
+def dTV_FGP_CPU(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM):
+    if inputData.ndim == 2:
+        return dTV_FGP_2D(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM)
+    elif inputData.ndim == 3:
+        return dTV_FGP_3D(inputData, refdata, regularisation_parameter, iterationsNumb, tolerance_param, eta_const, methodTV, nonneg, printM)
+
+def dTV_FGP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+               np.ndarray[np.float32_t, ndim=2, mode="c"] refdata,
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run FGP-dTV iterations for 2D data */
+    dTV_FGP_CPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0], regularisation_parameter, 
+                       iterationsNumb, 
+                       tolerance_param,
+                       eta_const,
+                       methodTV,                       
+                       nonneg,
+                       printM,
+                       dims[1], dims[0], 1)
+    
+    return outputData        
+            
+def dTV_FGP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+               np.ndarray[np.float32_t, ndim=3, mode="c"] refdata,
+                     float regularisation_parameter,
+                     int iterationsNumb, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0], dims[1], dims[2]], dtype='float32')
+           
+    #/* Run FGP-dTV iterations for 3D data */
+    dTV_FGP_CPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0], regularisation_parameter,
+                       iterationsNumb, 
+                       tolerance_param,
+                       eta_const,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[2], dims[1], dims[0])
+    return outputData
+    
+#****************************************************************#
+#*********************Total Nuclear Variation********************#
+#****************************************************************#
+def TNV_CPU(inputData, regularisation_parameter, iterationsNumb, tolerance_param):
+    if inputData.ndim == 2:
+        return 
+    elif inputData.ndim == 3:
+        return TNV_3D(inputData, regularisation_parameter, iterationsNumb, tolerance_param)
+
+def TNV_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterationsNumb,
+                     float tolerance_param):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+           
+    # Run TNV iterations for 3D (X,Y,Channels) data 
+    TNV_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, iterationsNumb, tolerance_param, dims[2], dims[1], dims[0])
+    return outputData
+#****************************************************************#
+#***************Nonlinear (Isotropic) Diffusion******************#
+#****************************************************************#
+def NDF_CPU(inputData, regularisation_parameter, edge_parameter, iterationsNumb,time_marching_parameter, penalty_type):
+    if inputData.ndim == 2:
+        return NDF_2D(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+    elif inputData.ndim == 3:
+        return NDF_3D(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+
+def NDF_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,                     
+                     float time_marching_parameter,
+                     int penalty_type):
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')   
+    
+    # Run Nonlinear Diffusion iterations for 2D data 
+    Diffusion_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)
+    return outputData
+            
+def NDF_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,                     
+                     float time_marching_parameter,
+                     int penalty_type):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+    
+    # Run Nonlinear Diffusion iterations for  3D data 
+    Diffusion_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])
+
+    return outputData
+
+#****************************************************************#
+#*************Anisotropic Fourth-Order diffusion*****************#
+#****************************************************************#
+def Diff4th_CPU(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter):
+    if inputData.ndim == 2:
+        return Diff4th_2D(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter)
+    elif inputData.ndim == 3:
+        return Diff4th_3D(inputData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter)
+
+def Diff4th_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,                     
+                     float time_marching_parameter):
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')   
+    
+    # Run Anisotropic Fourth-Order diffusion for 2D data 
+    Diffus4th_CPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[1], dims[0], 1)
+    return outputData
+          
+def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+    
+    # Run Anisotropic Fourth-Order diffusion for  3D data 
+    Diffus4th_CPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0])
+
+    return outputData
+
+#****************************************************************#
+#***************Patch-based weights calculation******************#
+#****************************************************************#
+def PATCHSEL_CPU(inputData, searchwindow, patchwindow, neighbours, edge_parameter):
+    if inputData.ndim == 2:
+        return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+    elif inputData.ndim == 3:
+        return 1
+def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+                     int searchwindow,
+                     int patchwindow,
+                     int neighbours,
+                     float edge_parameter):
+    cdef long dims[3]
+    dims[0] = neighbours
+    dims[1] = inputData.shape[0]
+    dims[2] = inputData.shape[1]
+    
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='float32')
+    
+    cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+            
+    cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+
+    # Run patch-based weight selection function
+    PatchSelect_CPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], 0, searchwindow, patchwindow,  neighbours,  edge_parameter, 1)
+    return H_i, H_j, Weights
+"""
+def PatchSel_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+                     int searchwindow,
+                     int patchwindow,
+                     int neighbours,
+                     float edge_parameter):
+    cdef long dims[4]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    dims[3] = neighbours
+    
+    cdef np.ndarray[np.float32_t, ndim=4, mode="c"] Weights = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='float32')
+    
+    cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_i = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+            
+    cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_j = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+            
+    cdef np.ndarray[np.uint16_t, ndim=4, mode="c"] H_k = \
+            np.zeros([dims[3],dims[0],dims[1],dims[2]], dtype='uint16')
+
+    # Run patch-based weight selection function
+    PatchSelect_CPU_main(&inputData[0,0,0], &H_i[0,0,0,0], &H_j[0,0,0,0], &H_k[0,0,0,0], &Weights[0,0,0,0], dims[2], dims[1], dims[0], searchwindow, patchwindow,  neighbours, edge_parameter, 1)
+    return H_i, H_j, H_k, Weights
+"""
+
+#****************************************************************#
+#***************Non-local Total Variation******************#
+#****************************************************************#
+def NLTV_CPU(inputData, H_i, H_j, H_k, Weights, regularisation_parameter, iterations):
+    if inputData.ndim == 2:
+        return NLTV_2D(inputData, H_i, H_j, Weights, regularisation_parameter, iterations)
+    elif inputData.ndim == 3:
+        return 1
+def NLTV_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+                     np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i,
+                     np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j,
+                     np.ndarray[np.float32_t, ndim=3, mode="c"] Weights,
+                     float regularisation_parameter,
+                     int iterations):
+
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    neighbours = H_i.shape[0]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+    
+    # Run nonlocal TV regularisation
+    Nonlocal_TV_CPU_main(&inputData[0,0], &outputData[0,0], &H_i[0,0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[1], dims[0], 0, neighbours, regularisation_parameter, iterations)
+    return outputData
+
+#*********************Inpainting WITH****************************#
+#***************Nonlinear (Isotropic) Diffusion******************#
+#****************************************************************#
+def NDF_INPAINT_CPU(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type):
+    if inputData.ndim == 2:
+        return NDF_INP_2D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+    elif inputData.ndim == 3:
+        return NDF_INP_3D(inputData, maskData, regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type)
+
+def NDF_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter,
+                     int penalty_type):
+
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+    
+    # Run Inpaiting by Diffusion iterations for 2D data 
+    Diffusion_Inpaint_CPU_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)
+    return outputData
+            
+def NDF_INP_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     np.ndarray[np.uint8_t, ndim=3, mode="c"] maskData,
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter,
+                     int penalty_type):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+    
+    # Run Inpaiting by Diffusion iterations for 3D data 
+    Diffusion_Inpaint_CPU_main(&inputData[0,0,0], &maskData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])
+
+    return outputData
+#*********************Inpainting WITH****************************#
+#***************Nonlocal Vertical Marching method****************#
+#****************************************************************#
+def NVM_INPAINT_CPU(inputData, maskData, SW_increment, iterationsNumb):
+    if inputData.ndim == 2:
+        return NVM_INP_2D(inputData, maskData, SW_increment, iterationsNumb)
+    elif inputData.ndim == 3:
+        return 
+
+def NVM_INP_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+               np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData,
+                     int SW_increment,
+                     int iterationsNumb):
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')   
+    
+    cdef np.ndarray[np.uint8_t, ndim=2, mode="c"] maskData_upd = \
+            np.zeros([dims[0],dims[1]], dtype='uint8')
+    
+    # Run Inpaiting by Nonlocal vertical marching method for 2D data 
+    NonlocalMarching_Inpaint_main(&inputData[0,0], &maskData[0,0], &outputData[0,0], 
+                                  &maskData_upd[0,0],
+                                  SW_increment, iterationsNumb, 1, dims[1], dims[0], 1)
+    
+    return (outputData, maskData_upd)
+
+
+#****************************************************************#
+#***************Calculation of TV-energy functional**************#
+#****************************************************************#
+def TV_ENERGY(inputData, inputData0, regularisation_parameter, typeFunctional):
+    if inputData.ndim == 2:
+        return TV_ENERGY_2D(inputData, inputData0, regularisation_parameter, typeFunctional)
+    elif inputData.ndim == 3:
+        return TV_ENERGY_3D(inputData, inputData0, regularisation_parameter, typeFunctional)
+
+def TV_ENERGY_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                 np.ndarray[np.float32_t, ndim=2, mode="c"] inputData0, 
+                     float regularisation_parameter,
+                     int typeFunctional):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=1, mode="c"] outputData = \
+            np.zeros([1], dtype='float32')
+                   
+    # run function    
+    TV_energy2D(&inputData[0,0], &inputData0[0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[1], dims[0])
+    
+    return outputData
+            
+def TV_ENERGY_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData,
+                 np.ndarray[np.float32_t, ndim=3, mode="c"] inputData0, 
+                     float regularisation_parameter,
+                     int typeFunctional):
+						 
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=1, mode="c"] outputData = \
+            np.zeros([1], dtype='float32')
+           
+    # Run function
+    TV_energy3D(&inputData[0,0,0], &inputData0[0,0,0], &outputData[0], regularisation_parameter, typeFunctional, dims[2], dims[1], dims[0])
+
+    return outputData
diff --git a/src/Python/src/gpu_regularisers.pyx b/src/Python/src/gpu_regularisers.pyx
new file mode 100644
index 0000000..b52f669
--- /dev/null
+++ b/src/Python/src/gpu_regularisers.pyx
@@ -0,0 +1,640 @@
+# distutils: language=c++
+"""
+Copyright 2018 CCPi
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Author: Edoardo Pasca, Daniil Kazantsev
+"""
+
+import cython
+import numpy as np
+cimport numpy as np
+
+CUDAErrorMessage = 'CUDA error'
+
+cdef extern int TV_ROF_GPU_main(float* Input, float* Output, float lambdaPar, int iter, float tau, int N, int M, int Z);
+cdef extern int TV_FGP_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int nonneg, int printM, int N, int M, int Z);
+cdef extern int TV_SB_GPU_main(float *Input, float *Output, float lambdaPar, int iter, float epsil, int methodTV, int printM, int N, int M, int Z);
+cdef extern int TGV_GPU_main(float *Input, float *Output, float lambdaPar, float alpha1, float alpha0, int iterationsNumb, float L2, int dimX, int dimY, int dimZ);
+cdef extern int LLT_ROF_GPU_main(float *Input, float *Output, float lambdaROF, float lambdaLLT, int iterationsNumb, float tau, int N, int M, int Z);
+cdef extern int NonlDiff_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int penaltytype, int N, int M, int Z);
+cdef extern int dTV_FGP_GPU_main(float *Input, float *InputRef, float *Output, float lambdaPar, int iterationsNumb, float epsil, float eta, int methodTV, int nonneg, int printM, int N, int M, int Z);
+cdef extern int Diffus4th_GPU_main(float *Input, float *Output, float lambdaPar, float sigmaPar, int iterationsNumb, float tau, int N, int M, int Z);
+cdef extern int PatchSelect_GPU_main(float *Input, unsigned short *H_i, unsigned short *H_j, float *Weights, int N, int M, int SearchWindow, int SimilarWin, int NumNeighb, float h);
+
+# Total-variation Rudin-Osher-Fatemi (ROF)
+def TV_ROF_GPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     time_marching_parameter):
+    if inputData.ndim == 2:
+        return ROFTV2D(inputData, 
+                     regularisation_parameter,
+                     iterations,
+                     time_marching_parameter)
+    elif inputData.ndim == 3:
+        return ROFTV3D(inputData, 
+                     regularisation_parameter,
+                     iterations, 
+                     time_marching_parameter)
+                     
+# Total-variation Fast-Gradient-Projection (FGP)
+def TV_FGP_GPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     nonneg,
+                     printM):
+    if inputData.ndim == 2:
+        return FGPTV2D(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     nonneg,
+                     printM)
+    elif inputData.ndim == 3:
+        return FGPTV3D(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     nonneg,
+                     printM)
+# Total-variation Split Bregman (SB)
+def TV_SB_GPU(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     printM):
+    if inputData.ndim == 2:
+        return SBTV2D(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     printM)
+    elif inputData.ndim == 3:
+        return SBTV3D(inputData,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     methodTV,
+                     printM)
+# LLT-ROF model
+def LLT_ROF_GPU(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter):
+    if inputData.ndim == 2:
+        return LLT_ROF_GPU2D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+    elif inputData.ndim == 3:
+        return LLT_ROF_GPU3D(inputData, regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter)
+# Total Generilised Variation (TGV)
+def TGV_GPU(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst):
+    if inputData.ndim == 2:
+        return TGV2D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
+    elif inputData.ndim == 3:
+        return TGV3D(inputData, regularisation_parameter, alpha1, alpha0, iterations, LipshitzConst)
+# Directional Total-variation Fast-Gradient-Projection (FGP)
+def dTV_FGP_GPU(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM):
+    if inputData.ndim == 2:
+        return FGPdTV2D(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
+    elif inputData.ndim == 3:
+        return FGPdTV3D(inputData,
+                     refdata,
+                     regularisation_parameter,
+                     iterations, 
+                     tolerance_param,
+                     eta_const,
+                     methodTV,
+                     nonneg,
+                     printM)
+# Nonlocal Isotropic Diffusion (NDF)
+def NDF_GPU(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter,
+                     penalty_type):
+    if inputData.ndim == 2:
+        return NDF_GPU_2D(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter,
+                     penalty_type)
+    elif inputData.ndim == 3:
+        return NDF_GPU_3D(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter,
+                     penalty_type)
+# Anisotropic Fourth-Order diffusion
+def Diff4th_GPU(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter):
+    if inputData.ndim == 2:
+        return Diff4th_2D(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter)
+    elif inputData.ndim == 3:
+        return Diff4th_3D(inputData,
+                     regularisation_parameter,
+                     edge_parameter,
+                     iterations, 
+                     time_marching_parameter)
+                     
+#****************************************************************#
+#********************** Total-variation ROF *********************#
+#****************************************************************#
+def ROFTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here
+    if (TV_ROF_GPU_main(
+            &inputData[0,0], &outputData[0,0], 
+                       regularisation_parameter,
+                       iterations , 
+                       time_marching_parameter, 
+                       dims[1], dims[0], 1)==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+    
+def ROFTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (TV_ROF_GPU_main(
+            &inputData[0,0,0], &outputData[0,0,0], 
+                       regularisation_parameter,
+                       iterations , 
+                       time_marching_parameter, 
+                       dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+#****************************************************************#
+#********************** Total-variation FGP *********************#
+#****************************************************************#
+#******** Total-variation Fast-Gradient-Projection (FGP)*********#
+def FGPTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (TV_FGP_GPU_main(&inputData[0,0], &outputData[0,0],
+                       regularisation_parameter, 
+                       iterations, 
+                       tolerance_param,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[1], dims[0], 1)==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+    
+def FGPTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (TV_FGP_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
+                       regularisation_parameter , 
+                       iterations, 
+                       tolerance_param,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+#***************************************************************#
+#********************** Total-variation SB *********************#
+#***************************************************************#
+#*************** Total-variation Split Bregman (SB)*************#
+def SBTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     int methodTV,
+                     int printM):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (TV_SB_GPU_main(&inputData[0,0], &outputData[0,0],
+                       regularisation_parameter, 
+                       iterations, 
+                       tolerance_param,
+                       methodTV,
+                       printM,
+                       dims[1], dims[0], 1)==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+    
+def SBTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     int methodTV,
+                     int printM):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (TV_SB_GPU_main(&inputData[0,0,0], &outputData[0,0,0],
+                       regularisation_parameter , 
+                       iterations, 
+                       tolerance_param,
+                       methodTV,
+                       printM,
+                       dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+
+#***************************************************************#
+#************************ LLT-ROF model ************************#
+#***************************************************************#
+#************Joint LLT-ROF model for higher order **************#
+def LLT_ROF_GPU2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (LLT_ROF_GPU_main(&inputData[0,0], &outputData[0,0],regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[1],dims[0],1)==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+    
+def LLT_ROF_GPU3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameterROF,
+                     float regularisation_parameterLLT,
+                     int iterations, 
+                     float time_marching_parameter):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (LLT_ROF_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameterROF, regularisation_parameterLLT, iterations, time_marching_parameter, dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+
+#***************************************************************#
+#***************** Total Generalised Variation *****************#
+#***************************************************************#
+def TGV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+                         
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+                   
+    #/* Run TGV iterations for 2D data */
+    if (TGV_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter,
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[1],dims[0], 1)==0):
+        return outputData
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+def TGV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float alpha1,
+                     float alpha0,
+                     int iterationsNumb, 
+                     float LipshitzConst):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (TGV_GPU_main(
+            &inputData[0,0,0], &outputData[0,0,0], regularisation_parameter,
+                       alpha1,
+                       alpha0,
+                       iterationsNumb, 
+                       LipshitzConst,
+                       dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+
+#****************************************************************#
+#**************Directional Total-variation FGP ******************#
+#****************************************************************#
+#******** Directional TV Fast-Gradient-Projection (FGP)*********#
+def FGPdTV2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+             np.ndarray[np.float32_t, ndim=2, mode="c"] refdata,
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (dTV_FGP_GPU_main(&inputData[0,0], &refdata[0,0], &outputData[0,0],
+                       regularisation_parameter, 
+                       iterations, 
+                       tolerance_param,
+                       eta_const,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[1], dims[0], 1)==0):
+        return outputData
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+    
+def FGPdTV3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+             np.ndarray[np.float32_t, ndim=3, mode="c"] refdata, 
+                     float regularisation_parameter,
+                     int iterations, 
+                     float tolerance_param,
+                     float eta_const,
+                     int methodTV,
+                     int nonneg,
+                     int printM):
+    
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+		    np.zeros([dims[0],dims[1],dims[2]], dtype='float32')
+          
+    # Running CUDA code here    
+    if (dTV_FGP_GPU_main(&inputData[0,0,0], &refdata[0,0,0], &outputData[0,0,0],
+                       regularisation_parameter , 
+                       iterations, 
+                       tolerance_param,
+                       eta_const,
+                       methodTV,
+                       nonneg,
+                       printM,
+                       dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+
+#****************************************************************#
+#***************Nonlinear (Isotropic) Diffusion******************#
+#****************************************************************#
+def NDF_GPU_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,                     
+                     float time_marching_parameter,
+                     int penalty_type):
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+    
+    #rangecheck = penalty_type < 1 and penalty_type > 3
+    #if not rangecheck:
+#        raise ValueError('Choose penalty type as 1 for Huber, 2 - Perona-Malik, 3 - Tukey Biweight')
+    
+    # Run Nonlinear Diffusion iterations for 2D data 
+    # Running CUDA code here  
+    if (NonlDiff_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[1], dims[0], 1)==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+            
+def NDF_GPU_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,                     
+                     float time_marching_parameter,
+                     int penalty_type):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')    
+       
+    # Run Nonlinear Diffusion iterations for  3D data 
+    # Running CUDA code here  
+    if (NonlDiff_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, penalty_type, dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+#****************************************************************#
+#************Anisotropic Fourth-Order diffusion******************#
+#****************************************************************#
+def Diff4th_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter):
+    cdef long dims[2]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    
+    cdef np.ndarray[np.float32_t, ndim=2, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1]], dtype='float32')
+    
+    # Run Anisotropic Fourth-Order diffusion for 2D data 
+    # Running CUDA code here  
+    if (Diffus4th_GPU_main(&inputData[0,0], &outputData[0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[1], dims[0], 1)==0):
+        return outputData
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+            
+def Diff4th_3D(np.ndarray[np.float32_t, ndim=3, mode="c"] inputData, 
+                     float regularisation_parameter,
+                     float edge_parameter,
+                     int iterationsNumb,
+                     float time_marching_parameter):
+    cdef long dims[3]
+    dims[0] = inputData.shape[0]
+    dims[1] = inputData.shape[1]
+    dims[2] = inputData.shape[2]
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] outputData = \
+            np.zeros([dims[0],dims[1],dims[2]], dtype='float32')    
+       
+    # Run Anisotropic Fourth-Order diffusion for  3D data 
+    # Running CUDA code here  
+    if (Diffus4th_GPU_main(&inputData[0,0,0], &outputData[0,0,0], regularisation_parameter, edge_parameter, iterationsNumb, time_marching_parameter, dims[2], dims[1], dims[0])==0):
+        return outputData;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
+#****************************************************************#
+#************Patch-based weights pre-selection******************#
+#****************************************************************#
+def PATCHSEL_GPU(inputData, searchwindow, patchwindow, neighbours, edge_parameter):
+    if inputData.ndim == 2:
+        return PatchSel_2D(inputData, searchwindow, patchwindow, neighbours, edge_parameter)
+    elif inputData.ndim == 3:
+        return 1
+def PatchSel_2D(np.ndarray[np.float32_t, ndim=2, mode="c"] inputData,
+                     int searchwindow,
+                     int patchwindow,
+                     int neighbours,
+                     float edge_parameter):
+    cdef long dims[3]
+    dims[0] = neighbours
+    dims[1] = inputData.shape[0]
+    dims[2] = inputData.shape[1]    
+    
+    cdef np.ndarray[np.float32_t, ndim=3, mode="c"] Weights = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='float32')
+    
+    cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_i = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+            
+    cdef np.ndarray[np.uint16_t, ndim=3, mode="c"] H_j = \
+            np.zeros([dims[0], dims[1],dims[2]], dtype='uint16')
+
+    # Run patch-based weight selection function
+    if (PatchSelect_GPU_main(&inputData[0,0], &H_j[0,0,0], &H_i[0,0,0], &Weights[0,0,0], dims[2], dims[1], searchwindow, patchwindow,  neighbours,  edge_parameter)==0):
+        return H_i, H_j, Weights;
+    else:
+        raise ValueError(CUDAErrorMessage);
+
diff --git a/test/lena_gray_512.tif b/test/lena_gray_512.tif
new file mode 100644
index 0000000..f80cafc
--- /dev/null
+++ b/test/lena_gray_512.tif
diff --git a/test/test_ROF_TV.py b/test/test_ROF_TV.py
new file mode 100644
index 0000000..dda38b7
--- /dev/null
+++ b/test/test_ROF_TV.py
@@ -0,0 +1,127 @@
+import unittest
+import math
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV
+#, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from testroutines import *
+
+class TestRegularisers(unittest.TestCase):
+
+    def test_ROF_TV_CPU(self):
+        filename = os.path.join("lena_gray_512.tif")
+        plt = TiffReader()
+        # read image
+        Im = plt.imread(filename)
+        Im = np.asarray(Im, dtype='float32')
+
+        Im = Im / 255
+        perc = 0.05
+        u0 = Im + np.random.normal(loc=0,
+                                   scale=perc * Im,
+                                   size=np.shape(Im))
+        u_ref = Im + np.random.normal(loc=0,
+                                      scale=0.01 * Im,
+                                      size=np.shape(Im))
+
+        # map the u0 u0->u0>0
+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+        u0 = u0.astype('float32')
+        u_ref = u_ref.astype('float32')
+
+
+        # set parameters
+        pars = {'algorithm': ROF_TV, \
+                'input': u0, \
+                'regularisation_parameter': 0.04, \
+                'number_of_iterations': 2500, \
+                'time_marching_parameter': 0.00002
+                }
+        print("#############ROF TV CPU####################")
+        start_time = timeit.default_timer()
+        rof_cpu = ROF_TV(pars['input'],
+                         pars['regularisation_parameter'],
+                         pars['number_of_iterations'],
+                         pars['time_marching_parameter'], 'cpu')
+        rms = rmse(Im, rof_cpu)
+        pars['rmse'] = rms
+        txtstr = printParametersToString(pars)
+        txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time)
+        print(txtstr)
+
+        self.assertTrue(math.isclose(rms,0.02067839,rel_tol=1e-2))
+
+
+    def test_ROF_TV_CPU_vs_GPU(self):
+        # print ("tomas debug test function")
+        print(__name__)
+        self.fail("testfail2")
+        filename = os.path.join("lena_gray_512.tif")
+        plt = TiffReader()
+        # read image
+        Im = plt.imread(filename)
+        Im = np.asarray(Im, dtype='float32')
+
+        Im = Im / 255
+        perc = 0.05
+        u0 = Im + np.random.normal(loc=0,
+                                   scale=perc * Im,
+                                   size=np.shape(Im))
+        u_ref = Im + np.random.normal(loc=0,
+                                      scale=0.01 * Im,
+                                      size=np.shape(Im))
+
+        # map the u0 u0->u0>0
+        # f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+        u0 = u0.astype('float32')
+        u_ref = u_ref.astype('float32')
+
+        print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+        print("____________ROF-TV bench___________________")
+        print("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+        # set parameters
+        pars = {'algorithm': ROF_TV, \
+                'input': u0, \
+                'regularisation_parameter': 0.04, \
+                'number_of_iterations': 2500, \
+                'time_marching_parameter': 0.00002
+                }
+        print("##############ROF TV GPU##################")
+        start_time = timeit.default_timer()
+        try:
+            rof_gpu = ROF_TV(pars['input'],
+                             pars['regularisation_parameter'],
+                             pars['number_of_iterations'],
+                             pars['time_marching_parameter'], 'gpu')
+        except ValueError as ve:
+            self.skipTest("Results not comparable. GPU computing error.")
+
+        rms = rmse(Im, rof_gpu)
+        pars['rmse'] = rms
+        pars['algorithm'] = ROF_TV
+        txtstr = printParametersToString(pars)
+        txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time)
+        print(txtstr)
+
+        print("#############ROF TV CPU####################")
+        start_time = timeit.default_timer()
+        rof_cpu = ROF_TV(pars['input'],
+                         pars['regularisation_parameter'],
+                         pars['number_of_iterations'],
+                         pars['time_marching_parameter'], 'cpu')
+        rms = rmse(Im, rof_cpu)
+        pars['rmse'] = rms
+
+        txtstr = printParametersToString(pars)
+        txtstr += "%s = %.3fs" % ('elapsed time', timeit.default_timer() - start_time)
+        print(txtstr)
+        print("--------Compare the results--------")
+        tolerance = 1e-04
+        diff_im = np.zeros(np.shape(rof_cpu))
+        diff_im = abs(rof_cpu - rof_gpu)
+        diff_im[diff_im > tolerance] = 1
+        self.assertLessEqual(diff_im.sum(), 1)
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/test/testroutines.py b/test/testroutines.py
new file mode 100644
index 0000000..8da5c5e
--- /dev/null
+++ b/test/testroutines.py
@@ -0,0 +1,37 @@
+import numpy as np
+from PIL import Image
+
+class TiffReader(object):
+    def imread(self, filename):
+        return np.asarray(Image.open(filename))
+
+
+###############################################################################
+def printParametersToString(pars):
+    txt = r''
+    for key, value in pars.items():
+        if key == 'algorithm':
+            txt += "{0} = {1}".format(key, value.__name__)
+        elif key == 'input':
+            txt += "{0} = {1}".format(key, np.shape(value))
+        elif key == 'refdata':
+            txt += "{0} = {1}".format(key, np.shape(value))
+        else:
+            txt += "{0} = {1}".format(key, value)
+        txt += '\n'
+    return txt
+
+
+def nrmse(im1, im2):
+    rmse = np.sqrt(np.sum((im2 - im1) ** 2) / float(im1.size))
+    max_val = max(np.max(im1), np.max(im2))
+    min_val = min(np.min(im1), np.min(im2))
+    return 1 - (rmse / (max_val - min_val))
+
+
+def rmse(im1, im2):
+    rmse = np.sqrt(np.sum((im1 - im2) ** 2) / float(im1.size))
+    return rmse
+
+
+###############################################################################