restructured sources

32 files changed, 0 insertions, 2775 deletions

diff --git a/Wrappers/Matlab/CMakeLists.txt b/Wrappers/Matlab/CMakeLists.txt
deleted file mode 100755
index 0c26148..0000000
--- a/Wrappers/Matlab/CMakeLists.txt
+++ /dev/null
@@ -1,147 +0,0 @@
-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}/Wrappers/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}/Wrappers/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}/Wrappers/Matlab/mex_compile/regularisers_CPU/*.c"

-    #"${CMAKE_SOURCE_DIR}/Wrappers/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}/Wrappers/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/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
deleted file mode 100644
index 0c331a4..0000000
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ /dev/null
@@ -1,178 +0,0 @@
-% 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/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
deleted file mode 100644
index 14d3096..0000000
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ /dev/null
@@ -1,189 +0,0 @@
-% 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/Wrappers/Matlab/demos/demoMatlab_inpaint.m b/Wrappers/Matlab/demos/demoMatlab_inpaint.m
deleted file mode 100644
index 66f9c15..0000000
--- a/Wrappers/Matlab/demos/demoMatlab_inpaint.m
+++ /dev/null
@@ -1,35 +0,0 @@
-% 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/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
deleted file mode 100644
index 72a828e..0000000
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex_Linux.m
+++ /dev/null
@@ -1,81 +0,0 @@
-% 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/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m b/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
deleted file mode 100644
index 6f7541c..0000000
--- a/Wrappers/Matlab/mex_compile/compileCPU_mex_WINDOWS.m
+++ /dev/null
@@ -1,135 +0,0 @@
-% 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/Wrappers/Matlab/mex_compile/compileGPU_mex.m b/Wrappers/Matlab/mex_compile/compileGPU_mex.m
deleted file mode 100644
index dd1475c..0000000
--- a/Wrappers/Matlab/mex_compile/compileGPU_mex.m
+++ /dev/null
@@ -1,74 +0,0 @@
-% 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/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt b/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt
deleted file mode 100644
index e69de29..0000000
--- a/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt
+++ /dev/null
diff --git a/Wrappers/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c
deleted file mode 100644
index 66ea9be..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
deleted file mode 100644
index 642362f..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c
deleted file mode 100644
index 1a0c070..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c
+++ /dev/null
@@ -1,114 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
deleted file mode 100644
index ab45446..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
+++ /dev/null
@@ -1,82 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff.c
deleted file mode 100644
index ec35b8b..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff.c
+++ /dev/null
@@ -1,89 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c
deleted file mode 100644
index 9833392..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlDiff_Inp.c
+++ /dev/null
@@ -1,103 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c
deleted file mode 100644
index b3f2c98..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/NonlocalMarching_Inpaint.c
+++ /dev/null
@@ -1,84 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
deleted file mode 100644
index 014c0a0..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
+++ /dev/null
@@ -1,88 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
deleted file mode 100644
index f942539..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
+++ /dev/null
@@ -1,92 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/ROF_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
deleted file mode 100644
index 55ef2b1..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
+++ /dev/null
@@ -1,77 +0,0 @@
-
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/SB_TV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/SB_TV.c
deleted file mode 100644
index 8636322..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/SB_TV.c
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/TGV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/TGV.c
deleted file mode 100644
index aa4eed4..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/TGV.c
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
-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/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c
deleted file mode 100644
index acea75d..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/TNV.c
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c b/Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c
deleted file mode 100644
index d457f46..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_CPU/TV_energy.c
+++ /dev/null
@@ -1,72 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp
deleted file mode 100644
index 0cc042b..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp
+++ /dev/null
@@ -1,77 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
deleted file mode 100644
index c174e75..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
+++ /dev/null
@@ -1,97 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp
deleted file mode 100644
index 3f5a4b3..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
deleted file mode 100644
index e8da4ce..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
+++ /dev/null
@@ -1,83 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp
deleted file mode 100644
index 1cd0cdc..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp
+++ /dev/null
@@ -1,92 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
deleted file mode 100644
index bd01d55..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
+++ /dev/null
@@ -1,74 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp
deleted file mode 100644
index 9d1328f..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp
+++ /dev/null
@@ -1,91 +0,0 @@
-/*
- * 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/Wrappers/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp b/Wrappers/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
deleted file mode 100644
index edb551d..0000000
--- a/Wrappers/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
+++ /dev/null
@@ -1,79 +0,0 @@
-/*
-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/Wrappers/Matlab/supp/RMSE.m b/Wrappers/Matlab/supp/RMSE.m
deleted file mode 100644
index 002f776..0000000
--- a/Wrappers/Matlab/supp/RMSE.m
+++ /dev/null
@@ -1,7 +0,0 @@
-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/Wrappers/Matlab/supp/my_red_yellowMAP.mat b/Wrappers/Matlab/supp/my_red_yellowMAP.mat
deleted file mode 100644
index c2a5b87..0000000
--- a/Wrappers/Matlab/supp/my_red_yellowMAP.mat
+++ /dev/null