Merge pull request #104 from vais-ral/newdirstructure

New directory structure, Merged other changes. The build script checks old and new structure.

24 files changed, 4264 insertions, 0 deletions

diff --git a/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
new file mode 100644
index 0000000..01491d9
--- /dev/null
+++ b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads real tomographic data (stored at Zenodo)
+--- https://doi.org/10.5281/zenodo.2578893
+* Reconstructs using TomoRec software
+* Saves reconstructed images 
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+3. libtiff if one needs to save tiff images:
+    install pip install libtiff
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+import h5py
+from tomorec.supp.suppTools import normaliser
+import time
+
+# load dendritic projection data
+h5f = h5py.File('data/DendrData_3D.h5','r')
+dataRaw = h5f['dataRaw'][:]
+flats = h5f['flats'][:]
+darks = h5f['darks'][:]
+angles_rad = h5f['angles_rad'][:]
+h5f.close()
+#%%
+# normalise the data [detectorsVert, Projections, detectorsHoriz]
+data_norm = normaliser(dataRaw, flats, darks, log='log')
+del dataRaw, darks, flats
+
+intens_max = 2.3
+plt.figure() 
+plt.subplot(131)
+plt.imshow(data_norm[:,150,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (analytical)')
+plt.subplot(132)
+plt.imshow(data_norm[300,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(data_norm[:,:,600],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+
+detectorHoriz = np.size(data_norm,2)
+det_y_crop = [i for i in range(0,detectorHoriz-22)]
+N_size = 950 # reconstruction domain
+time_label = int(time.time())
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("%%%%%%%%%%%%Reconstructing with FBP method %%%%%%%%%%%%%%%%%")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+from tomorec.methodsDIR import RecToolsDIR
+
+RectoolsDIR = RecToolsDIR(DetectorsDimH = np.size(det_y_crop),  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = 100,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = angles_rad, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    device='gpu')
+
+FBPrec = RectoolsDIR.FBP(data_norm[0:100,:,det_y_crop])
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(FBPrec[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(FBPrec[:,sliceSel,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(FBPrec[:,:,sliceSel],vmin=0, vmax=max_val, cmap="gray")
+plt.title('FBP Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+FBPrec += np.abs(np.min(FBPrec))
+multiplier = (int)(65535/(np.max(FBPrec)))
+
+# saving to tiffs (16bit)
+for i in range(0,np.size(FBPrec,0)):
+    tiff = TIFF.open('Dendr_FBP'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(FBPrec[i,:,:]*multiplier))
+    tiff.close()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH =  np.size(det_y_crop),  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = 100,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = angles_rad, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+                    nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+                    OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+                    tolerance = 1e-08, # tolerance to stop outer iterations earlier
+                    device='gpu')
+#%%
+print ("Reconstructing with ADMM method using SB-TV penalty")
+RecADMM_reg_sbtv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+                              rho_const = 2000.0, \
+                              iterationsADMM = 15, \
+                              regularisation = 'SB_TV', \
+                              regularisation_parameter = 0.00085,\
+                              regularisation_iterations = 50)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(RecADMM_reg_sbtv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_sbtv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_sbtv[:,:,sliceSel],vmin=0, vmax=max_val, cmap="gray")
+plt.title('3D ADMM-SB-TV Reconstruction, sagittal view')
+plt.show()
+
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_sbtv)))
+for i in range(0,np.size(RecADMM_reg_sbtv,0)):
+    tiff = TIFF.open('Dendr_ADMM_SBTV'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(RecADMM_reg_sbtv[i,:,:]*multiplier))
+    tiff.close()
+"""
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_SBTV'+str(time_label)+'.npy', RecADMM_reg_sbtv)
+del RecADMM_reg_sbtv
+#%%
+print ("Reconstructing with ADMM method using ROF-LLT penalty")
+RecADMM_reg_rofllt = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+                              rho_const = 2000.0, \
+                              iterationsADMM = 15, \
+                              regularisation = 'LLT_ROF', \
+                              regularisation_parameter = 0.0009,\
+                              regularisation_parameter2 = 0.0007,\
+                              time_marching_parameter = 0.001,\
+                              regularisation_iterations = 550)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(RecADMM_reg_rofllt[sliceSel,:,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_rofllt[:,sliceSel,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_rofllt[:,:,sliceSel],vmin=0, vmax=max_val)
+plt.title('3D ADMM-ROFLLT Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_rofllt)))
+for i in range(0,np.size(RecADMM_reg_rofllt,0)):
+    tiff = TIFF.open('Dendr_ADMM_ROFLLT'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(RecADMM_reg_rofllt[i,:,:]*multiplier))
+    tiff.close()
+"""
+
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_ROFLLT'+str(time_label)+'.npy', RecADMM_reg_rofllt)
+del RecADMM_reg_rofllt
+#%%
+print ("Reconstructing with ADMM method using TGV penalty")
+RecADMM_reg_tgv = RectoolsIR.ADMM(data_norm[0:100,:,det_y_crop],
+                              rho_const = 2000.0, \
+                              iterationsADMM = 15, \
+                              regularisation = 'TGV', \
+                              regularisation_parameter = 0.01,\
+                              regularisation_iterations = 500)
+
+sliceSel = 50
+max_val = 0.003
+plt.figure() 
+plt.subplot(131)
+plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, axial view')
+
+plt.subplot(132)
+plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, coronal view')
+
+plt.subplot(133)
+plt.imshow(RecADMM_reg_tgv[:,:,sliceSel],vmin=0, vmax=max_val)
+plt.title('3D ADMM-TGV Reconstruction, sagittal view')
+plt.show()
+
+# saving to tiffs (16bit)
+"""
+from libtiff import TIFF
+multiplier = (int)(65535/(np.max(RecADMM_reg_tgv)))
+for i in range(0,np.size(RecADMM_reg_tgv,0)):
+    tiff = TIFF.open('Dendr_ADMM_TGV'+'_'+str(i)+'.tiff', mode='w')
+    tiff.write_image(np.uint16(RecADMM_reg_tgv[i,:,:]*multiplier))
+    tiff.close()
+"""
+# Saving recpnstructed data with a unique time label
+np.save('Dendr_ADMM_TGV'+str(time_label)+'.npy', RecADMM_reg_tgv)
+del RecADMM_reg_tgv
+#%%
\ No newline at end of file
diff --git a/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py b/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
new file mode 100644
index 0000000..59ffc0e
--- /dev/null
+++ b/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
@@ -0,0 +1,161 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads data which is previosly generated by TomoPhantom software (Zenodo link)
+--- https://doi.org/10.5281/zenodo.2578893
+* Optimises for the regularisation parameters which later used in the script:
+Demo_SimulData_Recon_SX.py
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+#import timeit
+import matplotlib.pyplot as plt
+import numpy as np
+import h5py
+from ccpi.supp.qualitymetrics import QualityTools
+
+# loading the data 
+h5f = h5py.File('data/TomoSim_data1550671417.h5','r')
+phantom = h5f['phantom'][:]
+projdata_norm = h5f['projdata_norm'][:]
+proj_angles = h5f['proj_angles'][:]
+h5f.close()
+
+[Vert_det, AnglesNum, Horiz_det] = np.shape(projdata_norm)
+N_size = Vert_det
+
+sliceSel = 128
+#plt.gray()
+plt.figure() 
+plt.subplot(131)
+plt.imshow(phantom[sliceSel,:,:],vmin=0, vmax=1)
+plt.title('3D Phantom, axial view')
+
+plt.subplot(132)
+plt.imshow(phantom[:,sliceSel,:],vmin=0, vmax=1)
+plt.title('3D Phantom, coronal view')
+
+plt.subplot(133)
+plt.imshow(phantom[:,:,sliceSel],vmin=0, vmax=1)
+plt.title('3D Phantom, sagittal view')
+plt.show()
+
+intens_max = 240
+plt.figure() 
+plt.subplot(131)
+plt.imshow(projdata_norm[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (erroneous)')
+plt.subplot(132)
+plt.imshow(projdata_norm[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projdata_norm[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = Vert_det,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = proj_angles, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+                    nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+                    OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+                    tolerance = 1e-08, # tolerance to stop outer iterations earlier
+                    device='gpu')
+#%%
+param_space = 30
+reg_param_sb_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_sbtv = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using SB-TV penalty")
+for i in range(0,param_space):
+    RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 15, \
+                                  regularisation = 'SB_TV', \
+                                  regularisation_parameter = reg_param_sb_vec[i],\
+                                  regularisation_iterations = 50)
+    # calculate errors 
+    Qtools = QualityTools(phantom, RecADMM_reg_sbtv)
+    erros_vec_sbtv[i] = Qtools.rmse()
+    print("RMSE for regularisation parameter {} for ADMM-SB-TV is {}".format(reg_param_sb_vec[i],erros_vec_sbtv[i]))
+
+plt.figure() 
+plt.plot(erros_vec_sbtv)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_sbtv.h5', 'w')
+h5f.create_dataset('reg_param_sb_vec', data=reg_param_sb_vec)
+h5f.create_dataset('erros_vec_sbtv', data=erros_vec_sbtv)
+h5f.close()
+#%%
+param_space = 30
+reg_param_rofllt_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_rofllt = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using ROF-LLT penalty")
+for i in range(0,param_space):
+    RecADMM_reg_rofllt = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 15, \
+                                  regularisation = 'LLT_ROF', \
+                                  regularisation_parameter = reg_param_rofllt_vec[i],\
+                                  regularisation_parameter2 = 0.005,\
+                                  regularisation_iterations = 600)
+    # calculate errors 
+    Qtools = QualityTools(phantom, RecADMM_reg_rofllt)
+    erros_vec_rofllt[i] = Qtools.rmse()
+    print("RMSE for regularisation parameter {} for ADMM-ROF-LLT is {}".format(reg_param_rofllt_vec[i],erros_vec_rofllt[i]))
+
+plt.figure() 
+plt.plot(erros_vec_rofllt)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_rofllt.h5', 'w')
+h5f.create_dataset('reg_param_rofllt_vec', data=reg_param_rofllt_vec)
+h5f.create_dataset('erros_vec_rofllt', data=erros_vec_rofllt)
+h5f.close()
+#%%
+param_space = 30
+reg_param_tgv_vec = np.linspace(0.03,0.15,param_space,dtype='float32') # a vector of parameters
+erros_vec_tgv = np.zeros((param_space)) # a vector of errors
+
+print ("Reconstructing with ADMM method using TGV penalty")
+for i in range(0,param_space):
+    RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 15, \
+                                  regularisation = 'TGV', \
+                                  regularisation_parameter = reg_param_tgv_vec[i],\
+                                  regularisation_iterations = 600)
+    # calculate errors 
+    Qtools = QualityTools(phantom, RecADMM_reg_tgv)
+    erros_vec_tgv[i] = Qtools.rmse()
+    print("RMSE for regularisation parameter {} for ADMM-TGV is {}".format(reg_param_tgv_vec[i],erros_vec_tgv[i]))
+
+plt.figure() 
+plt.plot(erros_vec_tgv)
+
+# Saving generated data with a unique time label
+h5f = h5py.File('Optim_admm_tgv.h5', 'w')
+h5f.create_dataset('reg_param_tgv_vec', data=reg_param_tgv_vec)
+h5f.create_dataset('erros_vec_tgv', data=erros_vec_tgv)
+h5f.close()
+#%%
\ No newline at end of file
diff --git a/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py b/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
new file mode 100644
index 0000000..93b0cef
--- /dev/null
+++ b/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
@@ -0,0 +1,309 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Reads data which is previously generated by TomoPhantom software (Zenodo link)
+--- https://doi.org/10.5281/zenodo.2578893
+* Reconstruct using optimised regularisation parameters (see Demo_SimulData_ParOptimis_SX.py)
+____________________________________________________________________________
+>>>>> Dependencies: <<<<<
+1. ASTRA toolbox: conda install -c astra-toolbox astra-toolbox
+2. TomoRec: conda install -c dkazanc tomorec
+or install from https://github.com/dkazanc/TomoRec
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+GPLv3 license (ASTRA toolbox)
+"""
+#import timeit
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+import numpy as np
+import h5py
+from ccpi.supp.qualitymetrics import QualityTools
+from scipy.signal import gaussian
+
+# loading the data 
+h5f = h5py.File('data/TomoSim_data1550671417.h5','r')
+phantom = h5f['phantom'][:]
+projdata_norm = h5f['projdata_norm'][:]
+proj_angles = h5f['proj_angles'][:]
+h5f.close()
+
+[Vert_det, AnglesNum, Horiz_det] = np.shape(projdata_norm)
+N_size = Vert_det
+
+# loading optmisation parameters (the result of running Demo_SimulData_ParOptimis_SX)
+h5f = h5py.File('optim_param/Optim_admm_sbtv.h5','r')
+reg_param_sb_vec = h5f['reg_param_sb_vec'][:]
+erros_vec_sbtv = h5f['erros_vec_sbtv'][:]
+h5f.close()
+
+h5f = h5py.File('optim_param/Optim_admm_rofllt.h5','r')
+reg_param_rofllt_vec = h5f['reg_param_rofllt_vec'][:]
+erros_vec_rofllt = h5f['erros_vec_rofllt'][:]
+h5f.close()
+
+h5f = h5py.File('optim_param/Optim_admm_tgv.h5','r')
+reg_param_tgv_vec = h5f['reg_param_tgv_vec'][:]
+erros_vec_tgv = h5f['erros_vec_tgv'][:]
+h5f.close()
+
+index_minSBTV = min(xrange(len(erros_vec_sbtv)), key=erros_vec_sbtv.__getitem__)
+index_minROFLLT = min(xrange(len(erros_vec_rofllt)), key=erros_vec_rofllt.__getitem__)
+index_minTGV = min(xrange(len(erros_vec_tgv)), key=erros_vec_tgv.__getitem__)
+# assign optimal regularisation parameters:
+optimReg_sbtv = reg_param_sb_vec[index_minSBTV]
+optimReg_rofllt = reg_param_rofllt_vec[index_minROFLLT]
+optimReg_tgv = reg_param_tgv_vec[index_minTGV]
+#%%
+# plot loaded data
+sliceSel = 128
+#plt.figure() 
+fig, (ax1, ax2) = plt.subplots(figsize=(15, 5), ncols=2)
+plt.rcParams.update({'xtick.labelsize': 'x-small'})
+plt.rcParams.update({'ytick.labelsize':'x-small'})
+plt.subplot(121)
+one = plt.imshow(phantom[sliceSel,:,:],vmin=0, vmax=1, interpolation='none', cmap="PuOr")
+fig.colorbar(one, ax=ax1)
+plt.title('3D Phantom, axial (X-Y) view')
+plt.subplot(122)
+two = plt.imshow(phantom[:,sliceSel,:],vmin=0, vmax=1,interpolation='none', cmap="PuOr")
+fig.colorbar(two, ax=ax2)
+plt.title('3D Phantom, coronal (Y-Z) view')
+"""
+plt.subplot(133)
+plt.imshow(phantom[:,:,sliceSel],vmin=0, vmax=1, cmap="PuOr")
+plt.title('3D Phantom, sagittal view')
+
+"""
+plt.show()
+#%%
+intens_max = 220
+plt.figure() 
+plt.rcParams.update({'xtick.labelsize': 'x-small'})
+plt.rcParams.update({'ytick.labelsize':'x-small'})
+plt.subplot(131)
+plt.imshow(projdata_norm[:,sliceSel,:],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('X-detector', fontsize=16)
+plt.ylabel('Z-detector', fontsize=16)
+plt.title('2D Projection (X-Z) view', fontsize=19)
+plt.subplot(132)
+plt.imshow(projdata_norm[sliceSel,:,:],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('X-detector', fontsize=16)
+plt.ylabel('Projection angle', fontsize=16)
+plt.title('Sinogram (X-Y) view', fontsize=19)
+plt.subplot(133)
+plt.imshow(projdata_norm[:,:,sliceSel],vmin=0, vmax=intens_max, cmap="PuOr")
+plt.xlabel('Projection angle', fontsize=16)
+plt.ylabel('Z-detector', fontsize=16)
+plt.title('Vertical (Y-Z) view', fontsize=19)
+plt.show()
+#plt.savefig('projdata.pdf', format='pdf', dpi=1200)
+#%%
+# initialise TomoRec DIRECT reconstruction class ONCE
+from tomorec.methodsDIR import RecToolsDIR
+RectoolsDIR = RecToolsDIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = Vert_det,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = proj_angles, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    device = 'gpu')
+#%%
+print ("Reconstruction using FBP from TomoRec")
+recFBP= RectoolsDIR.FBP(projdata_norm) # FBP reconstruction
+#%%
+x0, y0 = 0, 127 # These are in _pixel_ coordinates!!
+x1, y1 = 255, 127
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,5))
+gs1 = gridspec.GridSpec(1, 3)
+gs1.update(wspace=0.1, hspace=0.05) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.imshow(recFBP[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax1.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.colorbar(ax=ax1)
+plt.title('FBP Reconstruction, axial (X-Y) view', fontsize=19)
+ax1.set_aspect('equal')
+ax3 = plt.subplot(gs1[1])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(recFBP[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax2 = plt.subplot(gs1[2])
+plt.imshow(recFBP[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('FBP Reconstruction, coronal (Y-Z) view', fontsize=19)
+ax2.set_aspect('equal')
+plt.show()
+#plt.savefig('FBP_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, recFBP)
+RMSE_fbp = Qtools.rmse()
+print("Root Mean Square Error for FBP is {}".format(RMSE_fbp))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, recFBP[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_fbp = Qtools.ssim(win2d)
+print("Mean SSIM for FBP is {}".format(ssim_fbp[0]))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Reconstructing with ADMM method using TomoRec software")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+# initialise TomoRec ITERATIVE reconstruction class ONCE
+from tomorec.methodsIR import RecToolsIR
+RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
+                    DetectorsDimV = Vert_det,  # DetectorsDimV # detector dimension (vertical) for 3D case only
+                    AnglesVec = proj_angles, # array of angles in radians
+                    ObjSize = N_size, # a scalar to define reconstructed object dimensions
+                    datafidelity='LS',# data fidelity, choose LS, PWLS (wip), GH (wip), Student (wip)
+                    nonnegativity='ENABLE', # enable nonnegativity constraint (set to 'ENABLE')
+                    OS_number = None, # the number of subsets, NONE/(or > 1) ~ classical / ordered subsets
+                    tolerance = 1e-08, # tolerance to stop outer iterations earlier
+                    device='gpu')
+#%%
+print ("Reconstructing with ADMM method using SB-TV penalty")
+RecADMM_reg_sbtv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 25, \
+                                  regularisation = 'SB_TV', \
+                                  regularisation_parameter = optimReg_sbtv,\
+                                  regularisation_iterations = 50)
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_sb_vec, erros_vec_sbtv, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_sbtv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-SBTV (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_sbtv[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_sbtv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-SBTV (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+plt.savefig('SBTV_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, RecADMM_reg_sbtv)
+RMSE_admm_sbtv = Qtools.rmse()
+print("Root Mean Square Error for ADMM-SB-TV is {}".format(RMSE_admm_sbtv))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_sbtv[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_sbtv = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-SBTV is {}".format(ssim_admm_sbtv[0]))
+#%%
+print ("Reconstructing with ADMM method using ROFLLT penalty")
+RecADMM_reg_rofllt = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 25, \
+                                  regularisation = 'LLT_ROF', \
+                                  regularisation_parameter = optimReg_rofllt,\
+                                  regularisation_parameter2 = 0.0085,\
+                                  regularisation_iterations = 600)
+
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_rofllt_vec, erros_vec_rofllt, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_rofllt[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-ROFLLT (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_rofllt[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_rofllt[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-ROFLLT (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+#plt.savefig('ROFLLT_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, RecADMM_reg_rofllt)
+RMSE_admm_rofllt = Qtools.rmse()
+print("Root Mean Square Error for ADMM-ROF-LLT is {}".format(RMSE_admm_rofllt))
+
+# SSIM measure
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_rofllt[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_rifllt = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-ROFLLT is {}".format(ssim_admm_rifllt[0]))
+#%%
+print ("Reconstructing with ADMM method using TGV penalty")
+RecADMM_reg_tgv = RectoolsIR.ADMM(projdata_norm,
+                                  rho_const = 2000.0, \
+                                  iterationsADMM = 25, \
+                                  regularisation = 'TGV', \
+                                  regularisation_parameter = optimReg_tgv,\
+                                  regularisation_iterations = 600)
+#%%
+sliceSel = int(0.5*N_size)
+max_val = 1
+plt.figure(figsize = (20,3))
+gs1 = gridspec.GridSpec(1, 4)
+gs1.update(wspace=0.02, hspace=0.01) # set the spacing between axes. 
+ax1 = plt.subplot(gs1[0])
+plt.plot(reg_param_tgv_vec, erros_vec_tgv, color='k',linewidth=2)
+plt.xlabel('Regularisation parameter', fontsize=16)
+plt.ylabel('RMSE value', fontsize=16)
+plt.title('Regularisation selection', fontsize=19)
+ax2 = plt.subplot(gs1[1])
+plt.imshow(RecADMM_reg_tgv[sliceSel,:,:],vmin=0, vmax=max_val, cmap="PuOr")
+ax2.plot([x0, x1], [y0, y1], 'ko-', linestyle='--')
+plt.title('ADMM-TGV (X-Y) view', fontsize=19)
+#ax2.set_aspect('equal')
+ax3 = plt.subplot(gs1[2])
+plt.plot(phantom[sliceSel,sliceSel,0:N_size],color='k',linewidth=2)
+plt.plot(RecADMM_reg_tgv[sliceSel,sliceSel,0:N_size],linestyle='--',color='g')
+plt.title('Profile', fontsize=19)
+ax4 = plt.subplot(gs1[3])
+plt.imshow(RecADMM_reg_tgv[:,sliceSel,:],vmin=0, vmax=max_val, cmap="PuOr")
+plt.title('ADMM-TGV (Y-Z) view', fontsize=19)
+plt.colorbar(ax=ax4)
+plt.show()
+#plt.savefig('TGV_phantom.pdf', format='pdf', dpi=1600)
+
+# calculate errors 
+Qtools = QualityTools(phantom, RecADMM_reg_tgv)
+RMSE_admm_tgv = Qtools.rmse()
+print("Root Mean Square Error for ADMM-TGV is {}".format(RMSE_admm_tgv))
+
+# SSIM measure
+#Create a 2d gaussian for the window parameter
+Qtools = QualityTools(phantom[128,:,:]*255, RecADMM_reg_tgv[128,:,:]*235)
+win = np.array([gaussian(11, 1.5)])
+win2d = win * (win.T)
+ssim_admm_tgv = Qtools.ssim(win2d)
+print("Mean SSIM ADMM-TGV is {}".format(ssim_admm_tgv[0]))
+#%%
\ No newline at end of file
diff --git a/demos/SoftwareX_supp/Demo_SimulData_SX.py b/demos/SoftwareX_supp/Demo_SimulData_SX.py
new file mode 100644
index 0000000..cdf4325
--- /dev/null
+++ b/demos/SoftwareX_supp/Demo_SimulData_SX.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This demo scripts support the following publication: 
+"CCPi-Regularisation Toolkit for computed tomographic image reconstruction with 
+proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner,
+ Philip J. Withers; Software X, 2019
+____________________________________________________________________________
+* Runs TomoPhantom software to simulate tomographic projection data with
+some imaging errors and noise
+* Saves the data into hdf file to be uploaded in reconstruction scripts
+__________________________________________________________________________
+
+>>>>> Dependencies: <<<<<
+1. TomoPhantom software for phantom and data generation
+
+@author: Daniil Kazantsev, e:mail daniil.kazantsev@diamond.ac.uk
+Apache 2.0 license
+"""
+import timeit
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+import tomophantom
+from tomophantom import TomoP3D
+from tomophantom.supp.flatsgen import flats
+from tomophantom.supp.normraw import normaliser_sim
+
+print ("Building 3D phantom using TomoPhantom software")
+tic=timeit.default_timer()
+model = 16 # select a model number from the library
+N_size = 256 # Define phantom dimensions using a scalar value (cubic phantom)
+path = os.path.dirname(tomophantom.__file__)
+path_library3D = os.path.join(path, "Phantom3DLibrary.dat")
+#This will generate a N_size x N_size x N_size phantom (3D)
+phantom_tm = TomoP3D.Model(model, N_size, path_library3D)
+toc=timeit.default_timer()
+Run_time = toc - tic
+print("Phantom has been built in {} seconds".format(Run_time))
+
+sliceSel = int(0.5*N_size)
+#plt.gray()
+plt.figure() 
+plt.subplot(131)
+plt.imshow(phantom_tm[sliceSel,:,:],vmin=0, vmax=1)
+plt.title('3D Phantom, axial view')
+
+plt.subplot(132)
+plt.imshow(phantom_tm[:,sliceSel,:],vmin=0, vmax=1)
+plt.title('3D Phantom, coronal view')
+
+plt.subplot(133)
+plt.imshow(phantom_tm[:,:,sliceSel],vmin=0, vmax=1)
+plt.title('3D Phantom, sagittal view')
+plt.show()
+
+# Projection geometry related parameters:
+Horiz_det = int(np.sqrt(2)*N_size) # detector column count (horizontal)
+Vert_det = N_size # detector row count (vertical) (no reason for it to be > N)
+angles_num = int(0.35*np.pi*N_size); # angles number
+angles = np.linspace(0.0,179.9,angles_num,dtype='float32') # in degrees
+angles_rad = angles*(np.pi/180.0)
+#%%
+print ("Building 3D analytical projection data with TomoPhantom")
+projData3D_analyt= TomoP3D.ModelSino(model, N_size, Horiz_det, Vert_det, angles, path_library3D)
+
+intens_max = N_size
+sliceSel = int(0.5*N_size)
+plt.figure() 
+plt.subplot(131)
+plt.imshow(projData3D_analyt[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (analytical)')
+plt.subplot(132)
+plt.imshow(projData3D_analyt[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projData3D_analyt[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+print ("Simulate flat fields, add noise and normalise projections...")
+flatsnum = 20 # generate 20 flat fields
+flatsSIM = flats(Vert_det, Horiz_det, maxheight = 0.1, maxthickness = 3, sigma_noise = 0.2, sigmasmooth = 3, flatsnum=flatsnum)
+
+plt.figure() 
+plt.imshow(flatsSIM[0,:,:],vmin=0, vmax=1)
+plt.title('A selected simulated flat-field')
+#%%
+# Apply normalisation of data and add noise
+flux_intensity = 60000 # controls the level of noise 
+sigma_flats = 0.01 # contro the level of noise in flats (higher creates more ring artifacts)
+projData3D_norm = normaliser_sim(projData3D_analyt, flatsSIM, sigma_flats, flux_intensity)
+
+intens_max = N_size
+sliceSel = int(0.5*N_size)
+plt.figure() 
+plt.subplot(131)
+plt.imshow(projData3D_norm[:,sliceSel,:],vmin=0, vmax=intens_max)
+plt.title('2D Projection (erroneous)')
+plt.subplot(132)
+plt.imshow(projData3D_norm[sliceSel,:,:],vmin=0, vmax=intens_max)
+plt.title('Sinogram view')
+plt.subplot(133)
+plt.imshow(projData3D_norm[:,:,sliceSel],vmin=0, vmax=intens_max)
+plt.title('Tangentogram view')
+plt.show()
+#%%
+import h5py
+import time
+time_label = int(time.time())
+# Saving generated data with a unique time label
+h5f = h5py.File('TomoSim_data'+str(time_label)+'.h5', 'w')
+h5f.create_dataset('phantom', data=phantom_tm)
+h5f.create_dataset('projdata_norm', data=projData3D_norm)
+h5f.create_dataset('proj_angles', data=angles_rad)
+h5f.close()
+#%%
\ No newline at end of file
diff --git a/demos/SoftwareX_supp/Readme.md b/demos/SoftwareX_supp/Readme.md
new file mode 100644
index 0000000..54e83f1
--- /dev/null
+++ b/demos/SoftwareX_supp/Readme.md
@@ -0,0 +1,26 @@
+
+# SoftwareX publication [1] supporting files
+
+## Decription:
+The scripts here support publication in SoftwareX journal [1] to ensure reproducibility of the research. The scripts linked with data shared at Zenodo. 
+
+## Data:
+Data is shared at Zenodo [here](https://doi.org/10.5281/zenodo.2578893)
+
+## Dependencies:
+1. [ASTRA toolbox](https://github.com/astra-toolbox/astra-toolbox): `conda install -c astra-toolbox astra-toolbox`
+2. [TomoRec](https://github.com/dkazanc/TomoRec): `conda install -c dkazanc tomorec`
+3. [Tomophantom](https://github.com/dkazanc/TomoPhantom): `conda install tomophantom -c ccpi`
+
+## Files description: 
+- `Demo_SimulData_SX.py` - simulates 3D projection data using [Tomophantom](https://github.com/dkazanc/TomoPhantom) software. One can skip this module if the data is taken from [Zenodo](https://doi.org/10.5281/zenodo.2578893)
+- `Demo_SimulData_ParOptimis_SX.py` - runs computationally extensive calculations for optimal regularisation parameters, the result are saved into directory `optim_param`. This script can be also skipped. 
+- `Demo_SimulData_Recon_SX.py` - using established regularisation parameters, one runs iterative reconstruction
+- `Demo_RealData_Recon_SX.py` - runs real data reconstructions. Can be quite intense on memory so reduce the size of the reconstructed volume if needed. 
+
+### References:
+[1] "CCPi-Regularisation Toolkit for computed tomographic image reconstruction with proximal splitting algorithms" by Daniil Kazantsev, Edoardo Pasca, Martin J. Turner and Philip J. Withers; SoftwareX, 2019. 
+
+### Acknowledgments:
+CCPi-RGL software is a product of the [CCPi](https://www.ccpi.ac.uk/) group, STFC SCD software developers and Diamond Light Source (DLS). Any relevant questions/comments can be e-mailed to Daniil Kazantsev at dkazanc@hotmail.com
+
diff --git a/demos/SoftwareX_supp/optim_param/Optim_admm_rofllt.h5 b/demos/SoftwareX_supp/optim_param/Optim_admm_rofllt.h5
new file mode 100644
index 0000000..63bc4fd
--- /dev/null
+++ b/demos/SoftwareX_supp/optim_param/Optim_admm_rofllt.h5
diff --git a/demos/SoftwareX_supp/optim_param/Optim_admm_sbtv.h5 b/demos/SoftwareX_supp/optim_param/Optim_admm_sbtv.h5
new file mode 100644
index 0000000..03c0c14
--- /dev/null
+++ b/demos/SoftwareX_supp/optim_param/Optim_admm_sbtv.h5
diff --git a/demos/SoftwareX_supp/optim_param/Optim_admm_tgv.h5 b/demos/SoftwareX_supp/optim_param/Optim_admm_tgv.h5
new file mode 100644
index 0000000..056d915
--- /dev/null
+++ b/demos/SoftwareX_supp/optim_param/Optim_admm_tgv.h5
diff --git a/demos/data/SinoInpaint.mat b/demos/data/SinoInpaint.mat
new file mode 100644
index 0000000..d748fb4
--- /dev/null
+++ b/demos/data/SinoInpaint.mat
diff --git a/demos/data/lena_gray_512.tif b/demos/data/lena_gray_512.tif
new file mode 100644
index 0000000..f80cafc
--- /dev/null
+++ b/demos/data/lena_gray_512.tif
diff --git a/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m
new file mode 100644
index 0000000..cf2c88a
--- /dev/null
+++ b/demos/demoMatlab_3Ddenoise.m
@@ -0,0 +1,186 @@
+% Volume (3D) denoising demo using CCPi-RGL
+clear; close all
+Path1 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'mex_compile' filesep 'installed'], 1i);
+Path2 = sprintf(['data' filesep], 1i);
+Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i);
+addpath(Path1);
+addpath(Path2);
+addpath(Path3);
+
+N = 512; 
+slices = 15;
+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(:,:,7), [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)');
+%%
+% fprintf('Denoise using the TGV model (GPU) \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_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
+% rmseTGV = RMSE(Ideal3D(:),u_tgv_gpu(:));
+% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
+% figure; imshow(u_tgv_gpu(:,:,3), [0 1]); title('TGV denoised volume (GPU)');
+%%
+%>>>>>>>>>>>>>> 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/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m
new file mode 100644
index 0000000..5e92ee1
--- /dev/null
+++ b/demos/demoMatlab_denoise.m
@@ -0,0 +1,209 @@
+% Image (2D) denoising demo using CCPi-RGL
+clear; close all
+fsep = '/';
+
+Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i);
+Path2 = sprintf([ data' fsep], 1i);
+Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep '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');
+
+%%
+fprintf('Denoise using the ROF-TV model (CPU) \n');
+lambda_reg = 0.017; % regularsation parameter for all methods
+tau_rof = 0.0025; % time-marching constant 
+iter_rof = 1200; % 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);
+[ssimval] = ssim(u_rof*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for ROF-TV is:', ssimval);
+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 = 1200; % 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');
+lambda_reg = 0.033;
+iter_fgp = 300; % number of FGP iterations
+epsil_tol =  1.0e-09; % 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);
+[ssimval] = ssim(u_fgp*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for FGP-TV is:', ssimval);
+figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
+%%
+% fprintf('Denoise using the FGP-TV model (GPU) \n');
+% iter_fgp = 300; % number of FGP iterations
+% epsil_tol =  1.0e-09; % 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 = 80; % number of SB iterations
+epsil_tol =  1.0e-08; % 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);
+[ssimval] = ssim(u_sb*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for SB-TV is:', ssimval);
+figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
+%%
+% fprintf('Denoise using the SB-TV model (GPU) \n');
+% iter_sb = 80; % 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 Nonlinear-Diffusion model (CPU) \n');
+iter_diff = 450; % number of diffusion iterations
+lambda_regDiff = 0.025; % regularisation for the diffusivity 
+sigmaPar = 0.015; % edge-preserving parameter
+tau_param = 0.02; % 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);
+[ssimval] = ssim(u_diff*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for NDF is:', ssimval);
+figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)');
+%%
+% fprintf('Denoise using Nonlinear-Diffusion model (GPU) \n');
+% iter_diff = 450; % 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 the TGV model (CPU) \n');
+lambda_TGV = 0.034; % regularisation parameter
+alpha1 = 1.0; % parameter to control the first-order term
+alpha0 = 1.0; % parameter to control the second-order term
+iter_TGV = 500; % 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);
+[ssimval] = ssim(u_tgv*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for TGV is:', ssimval);
+figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
+%%
+% fprintf('Denoise using the TGV model (GPU) \n');
+% lambda_TGV = 0.034; % regularisation parameter
+% alpha1 = 1.0; % parameter to control the first-order term
+% alpha0 = 1.0; % parameter to control the second-order term
+% iter_TGV = 500; % 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);
+% [ssimval] = ssim(u_tgv_gpu*255,single(Im)*255);
+% fprintf('%s %f \n', 'MSSIM error for TGV is:', ssimval);
+% figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)');
+%%
+fprintf('Denoise using the ROF-LLT model (CPU) \n');
+lambda_ROF = 0.016; % ROF regularisation parameter
+lambda_LLT = lambda_reg*0.25; % LLT regularisation parameter
+iter_LLT = 500; % 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);
+[ssimval] = ssim(u_rof_llt*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for ROFLLT is:', ssimval);
+figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)');
+%%
+% fprintf('Denoise using the ROF-LLT model (GPU) \n');
+% lambda_ROF = 0.016; % ROF regularisation parameter
+% lambda_LLT = lambda_reg*0.25; % 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 Fourth-order anisotropic diffusion model (CPU) \n');
+iter_diff = 800; % number of diffusion iterations
+lambda_regDiff = 3.5; % regularisation for the diffusivity 
+sigmaPar = 0.021; % 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);
+[ssimval] = ssim(u_diff4*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for DIFF4th is:', ssimval);
+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 = 0.055; % 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);
+[ssimval] = ssim(u_nltv*255,single(Im)*255);
+fprintf('%s %f \n', 'MSSIM error for NLTV is:', ssimval);
+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/demos/demoMatlab_inpaint.m b/demos/demoMatlab_inpaint.m
new file mode 100644
index 0000000..a85f2b9
--- /dev/null
+++ b/demos/demoMatlab_inpaint.m
@@ -0,0 +1,35 @@
+% Image (2D) inpainting demo using CCPi-RGL
+clear; close all
+Path1 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'mex_compile' filesep 'installed'], 1i);
+Path2 = sprintf(['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/demos/demo_cpu_inpainters.py b/demos/demo_cpu_inpainters.py
new file mode 100644
index 0000000..2e6ccf2
--- /dev/null
+++ b/demos/demo_cpu_inpainters.py
@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Demonstration of CPU inpainters
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from scipy import io
+from ccpi.filters.regularisers import NDF_INP, NVM_INP
+from ccpi.supp.qualitymetrics import QualityTools
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'maskData':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+
+# read sinogram and the mask
+filename = os.path.join("data" ,"SinoInpaint.mat")
+sino = io.loadmat(filename)
+sino_full = sino.get('Sinogram')
+Mask = sino.get('Mask')
+[angles_dim,detectors_dim] = sino_full.shape
+sino_full = sino_full/np.max(sino_full)
+#apply mask to sinogram
+sino_cut = sino_full*(1-Mask)
+#sino_cut_new = np.zeros((angles_dim,detectors_dim),'float32')
+#sino_cut_new = sino_cut.copy(order='c')
+#sino_cut_new[:] = sino_cut[:]
+sino_cut_new = np.ascontiguousarray(sino_cut, dtype=np.float32);
+#mask = np.zeros((angles_dim,detectors_dim),'uint8')
+#mask =Mask.copy(order='c')
+#mask[:] = Mask[:]
+mask = np.ascontiguousarray(Mask, dtype=np.uint8);
+
+plt.figure(1)
+plt.subplot(121)
+plt.imshow(sino_cut_new,vmin=0.0, vmax=1)
+plt.title('Missing Data sinogram')
+plt.subplot(122)
+plt.imshow(mask)
+plt.title('Mask')
+plt.show()
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Inpainting using linear diffusion (2D)__")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(2)
+plt.suptitle('Performance of linear inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut_new,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+        'input' : sino_cut_new,\
+        'maskData' : mask,\
+        'regularisation_parameter':5000,\
+        'edge_parameter':0,\
+        'number_of_iterations' :5000 ,\
+        'time_marching_parameter':0.000075,\
+        'penalty_type':0
+        }
+        
+start_time = timeit.default_timer()
+ndf_inp_linear = NDF_INP(pars['input'],
+              pars['maskData'],
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])
+
+Qtools = QualityTools(sino_full, ndf_inp_linear)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_inp_linear, cmap="gray")
+plt.title('{}'.format('Linear diffusion inpainting results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_Inpainting using nonlinear diffusion (2D)_")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(3)
+plt.suptitle('Performance of nonlinear diffusion inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut_new,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF_INP, \
+        'input' : sino_cut_new,\
+        'maskData' : mask,\
+        'regularisation_parameter':80,\
+        'edge_parameter':0.00009,\
+        'number_of_iterations' :1500 ,\
+        'time_marching_parameter':0.000008,\
+        'penalty_type':1
+        }
+        
+start_time = timeit.default_timer()
+ndf_inp_nonlinear = NDF_INP(pars['input'],
+              pars['maskData'],
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])
+             
+
+Qtools = QualityTools(sino_full, ndf_inp_nonlinear)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_inp_nonlinear, cmap="gray")
+plt.title('{}'.format('Nonlinear diffusion inpainting results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("Inpainting using nonlocal vertical marching")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure(4)
+plt.suptitle('Performance of NVM inpainting using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Missing data sinogram')
+imgplot = plt.imshow(sino_cut,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NVM_INP, \
+        'input' : sino_cut_new,\
+        'maskData' : mask,\
+        'SW_increment': 1,\
+        'number_of_iterations' : 150
+        }
+        
+start_time = timeit.default_timer()
+(nvm_inp, mask_upd) = NVM_INP(pars['input'],
+              pars['maskData'],
+              pars['SW_increment'],
+              pars['number_of_iterations'])
+             
+
+Qtools = QualityTools(sino_full, nvm_inp)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(nvm_inp, cmap="gray")
+plt.title('{}'.format('Nonlocal Vertical Marching inpainting results'))
+#%%
diff --git a/demos/demo_cpu_regularisers.py b/demos/demo_cpu_regularisers.py
new file mode 100644
index 0000000..d34607a
--- /dev/null
+++ b/demos/demo_cpu_regularisers.py
@@ -0,0 +1,572 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of CPU regularisers 
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, TNV, NDF, Diff4th
+from ccpi.filters.regularisers import PatchSelect, NLTV
+from ccpi.supp.qualitymetrics import QualityTools
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join( "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255.0
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+
+# change dims to check that modules work with non-squared images
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________ROF-TV (2D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of ROF-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.02,\
+        'number_of_iterations': 2000,\
+        'time_marching_parameter': 0.0025
+        }
+print ("#############ROF TV CPU####################")
+start_time = timeit.default_timer()
+rof_cpu = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+
+Qtools = QualityTools(Im, rof_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-TV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP TV CPU####################")
+start_time = timeit.default_timer()
+fgp_cpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')  
+             
+
+Qtools = QualityTools(Im, fgp_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________SB-TV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of SB-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############SB TV CPU####################")
+start_time = timeit.default_timer()
+sb_cpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'cpu')  
+             
+Qtools = QualityTools(Im, sb_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____Total Generalised Variation (2D)______")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :1350 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+             
+Qtools = QualityTools(Im, tgv_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("______________LLT- ROF (2D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+        
+print ("#############LLT- ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+Qtools = QualityTools(Im, lltrof_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("________________NDF (2D)___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NDF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : u0,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':1
+        }
+        
+print ("#############NDF CPU################")
+start_time = timeit.default_timer()
+ndf_cpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'cpu')  
+             
+Qtools = QualityTools(Im, ndf_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of Diff4th regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : u0,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############Diff4th CPU################")
+start_time = timeit.default_timer()
+diff4_cpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+             
+Qtools = QualityTools(Im, diff4_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal patches pre-calculation____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+start_time = timeit.default_timer()
+# set parameters
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+H_i, H_j, Weights = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'cpu')
+              
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+"""
+plt.figure()
+plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
+plt.show()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal Total Variation penalty____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NLTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars2 = {'algorithm' : NLTV, \
+        'input' : u0,\
+        'H_i': H_i, \
+        'H_j': H_j,\
+        'H_k' : 0,\
+        'Weights' : Weights,\
+        'regularisation_parameter': 0.04,\
+        'iterations': 3
+        }
+start_time = timeit.default_timer()
+nltv_cpu = NLTV(pars2['input'], 
+              pars2['H_i'],
+              pars2['H_j'], 
+              pars2['H_k'],
+              pars2['Weights'],
+              pars2['regularisation_parameter'],
+              pars2['iterations'])
+
+Qtools = QualityTools(Im, nltv_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(nltv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____________FGP-dTV (2D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dtv_cpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+Qtools = QualityTools(Im, fgp_dtv_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("__________Total nuclear Variation__________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TNV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+channelsNo = 5
+noisyVol = np.zeros((channelsNo,N,M),dtype='float32')
+idealVol = np.zeros((channelsNo,N,M),dtype='float32')
+
+for i in range (channelsNo):
+    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    idealVol[i,:,:] = Im
+
+# set parameters
+pars = {'algorithm' : TNV, \
+        'input' : noisyVol,\
+        'regularisation_parameter': 0.04, \
+        'number_of_iterations' : 200 ,\
+        'tolerance_constant':1e-05
+        }
+        
+print ("#############TNV CPU#################")
+start_time = timeit.default_timer()
+tnv_cpu = TNV(pars['input'],           
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'])
+
+Qtools = QualityTools(idealVol, tnv_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tnv_cpu[3,:,:], cmap="gray")
+plt.title('{}'.format('CPU results'))
diff --git a/demos/demo_cpu_regularisers3D.py b/demos/demo_cpu_regularisers3D.py
new file mode 100644
index 0000000..fd6c545
--- /dev/null
+++ b/demos/demo_cpu_regularisers3D.py
@@ -0,0 +1,463 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of 3D CPU regularisers 
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.supp.qualitymetrics import QualityTools
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join( "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+
+# change dims to check that modules work with non-squared images
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+slices = 15
+
+noisyVol = np.zeros((slices,N,M),dtype='float32')
+noisyRef = np.zeros((slices,N,M),dtype='float32')
+idealVol = np.zeros((slices,N,M),dtype='float32')
+
+for i in range (slices):
+    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
+    idealVol[i,:,:] = Im
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________ROF-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of ROF-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy 15th slice of a volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 500,\
+        'time_marching_parameter': 0.0025
+        }
+print ("#############ROF TV CPU####################")
+start_time = timeit.default_timer()
+rof_cpu3D = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+
+Qtools = QualityTools(idealVol, rof_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using ROF-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-TV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP TV CPU####################")
+start_time = timeit.default_timer()
+fgp_cpu3D = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')  
+             
+Qtools = QualityTools(idealVol, fgp_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using FGP-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________SB-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of SB-TV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############SB TV CPU####################")
+start_time = timeit.default_timer()
+sb_cpu3D = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'cpu')
+             
+
+Qtools = QualityTools(idealVol, sb_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using SB-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________LLT-ROF (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : noisyVol,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.015, \
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+
+print ("#############LLT ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu3D = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+
+Qtools = QualityTools(idealVol, lltrof_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using LLT-ROF'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________TGV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :250 ,\
+        'LipshitzConstant' :12 ,\
+        }
+
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu3D = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+
+Qtools = QualityTools(idealVol, tgv_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using TGV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("________________NDF (3D)___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NDF regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+        
+print ("#############NDF CPU################")
+start_time = timeit.default_timer()
+ndf_cpu3D = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'])  
+             
+
+Qtools = QualityTools(idealVol, ndf_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using NDF iterations'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of Diff4th regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : noisyVol,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############Diff4th CPU################")
+start_time = timeit.default_timer()
+diff4th_cpu3D = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'])  
+             
+
+Qtools = QualityTools(idealVol, diff4th_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4th_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using DIFF4th iterations'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-dTV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-dTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV,\
+        'input' : noisyVol,\
+        'refdata' : noisyRef,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dTV_cpu3D = FGP_dTV(pars['input'],
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'],
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+             
+
+Qtools = QualityTools(idealVol, fgp_dTV_cpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dTV_cpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the CPU using FGP-dTV'))
+#%%
diff --git a/demos/demo_cpu_vs_gpu_regularisers.py b/demos/demo_cpu_vs_gpu_regularisers.py
new file mode 100644
index 0000000..e1eb91f
--- /dev/null
+++ b/demos/demo_cpu_vs_gpu_regularisers.py
@@ -0,0 +1,794 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of CPU implementation against the GPU one
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.filters.regularisers import PatchSelect
+from ccpi.supp.qualitymetrics import QualityTools
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+
+filename = os.path.join("data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)                     
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________ROF-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of ROF-TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 4500,\
+        'time_marching_parameter': 0.00002
+        }
+print ("#############ROF TV CPU####################")
+start_time = timeit.default_timer()
+rof_cpu = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'cpu')
+
+Qtools = QualityTools(Im, rof_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############ROF TV GPU##################")
+start_time = timeit.default_timer()
+rof_gpu = ROF_TV(pars['input'], 
+                     pars['regularisation_parameter'],
+                     pars['number_of_iterations'], 
+                     pars['time_marching_parameter'],'gpu')
+
+Qtools = QualityTools(Im, rof_gpu)
+pars['rmse'] = Qtools.rmse()
+
+pars['algorithm'] = ROF_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(rof_cpu))
+diff_im = abs(rof_cpu - rof_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of FGP-TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :1200 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP TV CPU####################")
+start_time = timeit.default_timer()
+fgp_cpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')  
+             
+
+Qtools = QualityTools(Im, fgp_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############FGP TV GPU##################")
+start_time = timeit.default_timer()
+fgp_gpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+
+Qtools = QualityTools(Im, fgp_gpu)
+pars['rmse'] = Qtools.rmse()
+
+pars['algorithm'] = FGP_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(fgp_cpu))
+diff_im = abs(fgp_cpu - fgp_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________SB-TV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of SB-TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':1e-05,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############SB-TV CPU####################")
+start_time = timeit.default_timer()
+sb_cpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'cpu')  
+             
+
+Qtools = QualityTools(Im, sb_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############SB TV GPU##################")
+start_time = timeit.default_timer()
+sb_gpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'gpu')
+
+Qtools = QualityTools(Im, sb_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = SB_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(sb_cpu))
+diff_im = abs(sb_cpu - sb_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________TGV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of TGV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :400 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_cpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'cpu')
+             
+Qtools = QualityTools(Im, tgv_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############TGV GPU##################")
+start_time = timeit.default_timer()
+tgv_gpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')
+                                   
+Qtools = QualityTools(Im, tgv_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = TGV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(tgv_gpu))
+diff_im = abs(tgv_cpu - tgv_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________LLT-ROF bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of LLT-ROF regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :4500 ,\
+        'time_marching_parameter' :0.00002 ,\
+        }
+        
+print ("#############LLT- ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_cpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+Qtools = QualityTools(Im, lltrof_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("#############LLT- ROF GPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+
+Qtools = QualityTools(Im, lltrof_gpu)
+pars['rmse'] = Qtools.rmse()
+
+pars['algorithm'] = LLT_ROF
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(lltrof_gpu))
+diff_im = abs(lltrof_cpu - lltrof_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________NDF bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of NDF regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : u0,\
+        'regularisation_parameter':0.06, \
+        'edge_parameter':0.04,\
+        'number_of_iterations' :1000 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+        
+print ("#############NDF CPU####################")
+start_time = timeit.default_timer()
+ndf_cpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'cpu')
+
+Qtools = QualityTools(Im, ndf_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+
+print ("##############NDF GPU##################")
+start_time = timeit.default_timer()
+ndf_gpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'gpu')
+
+Qtools = QualityTools(Im, ndf_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = NDF
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(ndf_cpu))
+diff_im = abs(ndf_cpu - ndf_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of Diff4th regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : u0,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.001
+        }
+
+print ("#############Diff4th CPU####################")
+start_time = timeit.default_timer()
+diff4th_cpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'cpu')
+
+Qtools = QualityTools(Im, diff4th_cpu)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4th_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############Diff4th GPU##################")
+start_time = timeit.default_timer()
+diff4th_gpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 'gpu')
+
+Qtools = QualityTools(Im, diff4th_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = Diff4th
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4th_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(diff4th_cpu))
+diff_im = abs(diff4th_cpu - diff4th_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-dTV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of FGP-dTV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,4,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :1000 ,\
+        'tolerance_constant':1e-07,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+        
+print ("#############FGP dTV CPU####################")
+start_time = timeit.default_timer()
+fgp_dtv_cpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'cpu')
+
+Qtools = QualityTools(Im, fgp_dtv_cpu)
+pars['rmse'] = Qtools.rmse()
+
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+
+print ("##############FGP dTV GPU##################")
+start_time = timeit.default_timer()
+fgp_dtv_gpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+Qtools = QualityTools(Im, fgp_dtv_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = FGP_dTV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,4,3)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(fgp_dtv_cpu))
+diff_im = abs(fgp_dtv_cpu - fgp_dtv_gpu)
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,4,4)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____Non-local regularisation bench_________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Comparison of Nonlocal TV regulariser using CPU and GPU implementations')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+print ("############## Nonlocal Patches on CPU##################")
+start_time = timeit.default_timer()
+H_i, H_j, WeightsCPU = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'cpu')
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+
+print ("############## Nonlocal Patches on GPU##################")
+start_time = timeit.default_timer()
+start_time = timeit.default_timer()
+H_i, H_j, WeightsGPU = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'gpu')
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+
+print ("--------Compare the results--------")
+tolerance = 1e-05
+diff_im = np.zeros(np.shape(u0))
+diff_im = abs(WeightsCPU[0,:,:] - WeightsGPU[0,:,:])
+diff_im[diff_im > tolerance] = 1
+a=fig.add_subplot(1,2,2)
+imgplot = plt.imshow(diff_im, vmin=0, vmax=1, cmap="gray")
+plt.title('{}'.format('Pixels larger threshold difference'))
+if (diff_im.sum() > 1):
+    print ("Arrays do not match!")
+else:
+    print ("Arrays match")
+#%%
\ No newline at end of file
diff --git a/demos/demo_gpu_regularisers.py b/demos/demo_gpu_regularisers.py
new file mode 100644
index 0000000..89bb948
--- /dev/null
+++ b/demos/demo_gpu_regularisers.py
@@ -0,0 +1,512 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of GPU regularisers
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.filters.regularisers import PatchSelect, NLTV
+from ccpi.supp.qualitymetrics import QualityTools
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join( "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)                     
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________ROF-TV regulariser_____________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the ROF-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 1200,\
+        'time_marching_parameter': 0.0025
+        }
+print ("##############ROF TV GPU##################")
+start_time = timeit.default_timer()
+rof_gpu = ROF_TV(pars['input'], 
+                     pars['regularisation_parameter'],
+                     pars['number_of_iterations'], 
+                     pars['time_marching_parameter'],'gpu')
+
+Qtools = QualityTools(Im, rof_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = ROF_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-TV regulariser_____________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the FGP-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :1200 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("##############FGP TV GPU##################")
+start_time = timeit.default_timer()
+fgp_gpu = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+Qtools = QualityTools(Im, fgp_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = FGP_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________SB-TV regulariser______________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the SB-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :150 ,\
+        'tolerance_constant':1e-06,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("##############SB TV GPU##################")
+start_time = timeit.default_timer()
+sb_gpu = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'gpu')
+
+Qtools = QualityTools(Im, sb_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = SB_TV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_____Total Generalised Variation (2D)______")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : u0,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :1250 ,\
+        'LipshitzConstant' :12 ,\
+        }
+        
+print ("#############TGV CPU####################")
+start_time = timeit.default_timer()
+tgv_gpu = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')  
+
+Qtools = QualityTools(Im, tgv_gpu)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("______________LLT- ROF (2D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : u0,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.01, \
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+        
+print ("#############LLT- ROF GPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+             
+Qtools = QualityTools(Im, lltrof_gpu)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________NDF regulariser_____________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the NDF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : u0,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+
+print ("##############NDF GPU##################")
+start_time = timeit.default_timer()
+ndf_gpu = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'gpu')  
+
+Qtools = QualityTools(Im, ndf_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = NDF
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (2D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of Diff4th regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : u0,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############DIFF4th CPU################")
+start_time = timeit.default_timer()
+diff4_gpu = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+
+Qtools = QualityTools(Im, diff4_gpu)
+pars['algorithm'] = Diff4th
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal patches pre-calculation____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+start_time = timeit.default_timer()
+# set parameters
+pars = {'algorithm' : PatchSelect, \
+        'input' : u0,\
+        'searchwindow': 7, \
+        'patchwindow': 2,\
+        'neighbours' : 15 ,\
+        'edge_parameter':0.18}
+
+H_i, H_j, Weights = PatchSelect(pars['input'], 
+              pars['searchwindow'],
+              pars['patchwindow'], 
+              pars['neighbours'],
+              pars['edge_parameter'],'gpu')
+              
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+"""
+plt.figure()
+plt.imshow(Weights[0,:,:],cmap="gray",interpolation="nearest",vmin=0, vmax=1)
+plt.show()
+"""
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Nonlocal Total Variation penalty____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NLTV regulariser using the CPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+pars2 = {'algorithm' : NLTV, \
+        'input' : u0,\
+        'H_i': H_i, \
+        'H_j': H_j,\
+        'H_k' : 0,\
+        'Weights' : Weights,\
+        'regularisation_parameter': 0.02,\
+        'iterations': 3
+        }
+start_time = timeit.default_timer()
+nltv_cpu = NLTV(pars2['input'], 
+              pars2['H_i'],
+              pars2['H_j'], 
+              pars2['H_k'],
+              pars2['Weights'],
+              pars2['regularisation_parameter'],
+              pars2['iterations'])
+
+Qtools = QualityTools(Im, nltv_cpu)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(nltv_cpu, cmap="gray")
+plt.title('{}'.format('CPU results'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("____________FGP-dTV bench___________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of the FGP-dTV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(u0,cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : u0,\
+        'refdata' : u_ref,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :2000 ,\
+        'tolerance_constant':1e-06,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("##############FGP dTV GPU##################")
+start_time = timeit.default_timer()
+fgp_dtv_gpu = FGP_dTV(pars['input'], 
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+
+Qtools = QualityTools(Im, fgp_dtv_gpu)
+pars['rmse'] = Qtools.rmse()
+pars['algorithm'] = FGP_dTV
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dtv_gpu, cmap="gray")
+plt.title('{}'.format('GPU results'))
diff --git a/demos/demo_gpu_regularisers3D.py b/demos/demo_gpu_regularisers3D.py
new file mode 100644
index 0000000..be16921
--- /dev/null
+++ b/demos/demo_gpu_regularisers3D.py
@@ -0,0 +1,455 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb 22 11:39:43 2018
+
+Demonstration of GPU regularisers
+
+@authors: Daniil Kazantsev, Edoardo Pasca
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import os
+import timeit
+from ccpi.filters.regularisers import ROF_TV, FGP_TV, SB_TV, TGV, LLT_ROF, FGP_dTV, NDF, Diff4th
+from ccpi.supp.qualitymetrics import QualityTools
+###############################################################################
+def printParametersToString(pars):
+        txt = r''
+        for key, value in pars.items():
+            if key== 'algorithm' :
+                txt += "{0} = {1}".format(key, value.__name__)
+            elif key == 'input':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            elif key == 'refdata':
+                txt += "{0} = {1}".format(key, np.shape(value))
+            else:
+                txt += "{0} = {1}".format(key, value)
+            txt += '\n'
+        return txt
+###############################################################################
+#%%
+filename = os.path.join( "data" ,"lena_gray_512.tif")
+
+# read image
+Im = plt.imread(filename)                     
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+u0 = Im + np.random.normal(loc = 0 ,
+                                  scale = perc * Im , 
+                                  size = np.shape(Im))
+u_ref = Im + np.random.normal(loc = 0 ,
+                                  scale = 0.01 * Im , 
+                                  size = np.shape(Im))
+(N,M) = np.shape(u0)
+# map the u0 u0->u0>0
+# f = np.frompyfunc(lambda x: 0 if x < 0 else x, 1,1)
+u0 = u0.astype('float32')
+u_ref = u_ref.astype('float32')
+"""
+M = M-100
+u_ref2 = np.zeros([N,M],dtype='float32')
+u_ref2[:,0:M] = u_ref[:,0:M]
+u_ref = u_ref2
+del u_ref2
+
+u02 = np.zeros([N,M],dtype='float32')
+u02[:,0:M] = u0[:,0:M]
+u0 = u02
+del u02
+
+Im2 = np.zeros([N,M],dtype='float32')
+Im2[:,0:M] = Im[:,0:M]
+Im = Im2
+del Im2
+"""
+
+
+slices = 20
+
+filename = os.path.join( "data" ,"lena_gray_512.tif")
+Im = plt.imread(filename)
+Im = np.asarray(Im, dtype='float32')
+
+Im = Im/255
+perc = 0.05
+
+noisyVol = np.zeros((slices,N,N),dtype='float32')
+noisyRef = np.zeros((slices,N,N),dtype='float32')
+idealVol = np.zeros((slices,N,N),dtype='float32')
+
+for i in range (slices):
+    noisyVol[i,:,:] = Im + np.random.normal(loc = 0 , scale = perc * Im , size = np.shape(Im))
+    noisyRef[i,:,:] = Im + np.random.normal(loc = 0 , scale = 0.01 * Im , size = np.shape(Im))
+    idealVol[i,:,:] = Im
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________ROF-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of ROF-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy 15th slice of a volume')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm': ROF_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04,\
+        'number_of_iterations': 500,\
+        'time_marching_parameter': 0.0025        
+        }
+print ("#############ROF TV GPU####################")
+start_time = timeit.default_timer()
+rof_gpu3D = ROF_TV(pars['input'],
+             pars['regularisation_parameter'],
+             pars['number_of_iterations'],
+             pars['time_marching_parameter'],'gpu')
+
+Qtools = QualityTools(idealVol, rof_gpu3D)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(rof_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using ROF-TV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-TV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("#############FGP TV GPU####################")
+start_time = timeit.default_timer()
+fgp_gpu3D = FGP_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+
+Qtools = QualityTools(idealVol, fgp_gpu3D)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using FGP-TV'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________SB-TV (3D)__________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of SB-TV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : SB_TV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :100 ,\
+        'tolerance_constant':1e-05,\
+        'methodTV': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("#############SB TV GPU####################")
+start_time = timeit.default_timer()
+sb_gpu3D = SB_TV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['methodTV'],
+              pars['printingOut'],'gpu')
+
+Qtools = QualityTools(idealVol, sb_gpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(sb_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using SB-TV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________LLT-ROF (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of LLT-ROF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : LLT_ROF, \
+        'input' : noisyVol,\
+        'regularisation_parameterROF':0.04, \
+        'regularisation_parameterLLT':0.015, \
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter' :0.0025 ,\
+        }
+
+print ("#############LLT ROF CPU####################")
+start_time = timeit.default_timer()
+lltrof_gpu3D = LLT_ROF(pars['input'], 
+              pars['regularisation_parameterROF'],
+              pars['regularisation_parameterLLT'],
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+
+Qtools = QualityTools(idealVol, lltrof_gpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(lltrof_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using LLT-ROF'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________TGV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of TGV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : TGV, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.04, \
+        'alpha1':1.0,\
+        'alpha0':2.0,\
+        'number_of_iterations' :600 ,\
+        'LipshitzConstant' :12 ,\
+        }
+
+print ("#############TGV GPU####################")
+start_time = timeit.default_timer()
+tgv_gpu3D = TGV(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['alpha1'],
+              pars['alpha0'],
+              pars['number_of_iterations'],
+              pars['LipshitzConstant'],'gpu')
+
+Qtools = QualityTools(idealVol, tgv_gpu3D)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(tgv_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using TGV'))
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________NDF-TV (3D)_________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of NDF regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : NDF, \
+        'input' : noisyVol,\
+        'regularisation_parameter':0.025, \
+        'edge_parameter':0.015,\
+        'number_of_iterations' :500 ,\
+        'time_marching_parameter':0.025,\
+        'penalty_type':  1
+        }
+
+print ("#############NDF GPU####################")
+start_time = timeit.default_timer()
+ndf_gpu3D = NDF(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'], 
+              pars['penalty_type'],'gpu')
+
+Qtools = QualityTools(idealVol, ndf_gpu3D)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(ndf_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using NDF'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("___Anisotropic Diffusion 4th Order (3D)____")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of DIFF4th regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : Diff4th, \
+        'input' : noisyVol,\
+        'regularisation_parameter':3.5, \
+        'edge_parameter':0.02,\
+        'number_of_iterations' :300 ,\
+        'time_marching_parameter':0.0015
+        }
+        
+print ("#############DIFF4th CPU################")
+start_time = timeit.default_timer()
+diff4_gpu3D = Diff4th(pars['input'], 
+              pars['regularisation_parameter'],
+              pars['edge_parameter'], 
+              pars['number_of_iterations'],
+              pars['time_marching_parameter'],'gpu')
+
+Qtools = QualityTools(idealVol, diff4_gpu3D)
+pars['rmse'] = Qtools.rmse()
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(diff4_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('GPU results'))
+
+#%%
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+print ("_______________FGP-dTV (3D)________________")
+print ("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
+
+## plot 
+fig = plt.figure()
+plt.suptitle('Performance of FGP-dTV regulariser using the GPU')
+a=fig.add_subplot(1,2,1)
+a.set_title('Noisy Image')
+imgplot = plt.imshow(noisyVol[10,:,:],cmap="gray")
+
+# set parameters
+pars = {'algorithm' : FGP_dTV, \
+        'input' : noisyVol,\
+        'refdata' : noisyRef,\
+        'regularisation_parameter':0.04, \
+        'number_of_iterations' :300 ,\
+        'tolerance_constant':0.00001,\
+        'eta_const':0.2,\
+        'methodTV': 0 ,\
+        'nonneg': 0 ,\
+        'printingOut': 0 
+        }
+
+print ("#############FGP TV GPU####################")
+start_time = timeit.default_timer()
+fgp_dTV_gpu3D = FGP_dTV(pars['input'],
+              pars['refdata'], 
+              pars['regularisation_parameter'],
+              pars['number_of_iterations'],
+              pars['tolerance_constant'], 
+              pars['eta_const'],
+              pars['methodTV'],
+              pars['nonneg'],
+              pars['printingOut'],'gpu')
+
+Qtools = QualityTools(idealVol, fgp_dTV_gpu3D)
+pars['rmse'] = Qtools.rmse()
+
+txtstr = printParametersToString(pars)
+txtstr += "%s = %.3fs" % ('elapsed time',timeit.default_timer() - start_time)
+print (txtstr)
+a=fig.add_subplot(1,2,2)
+
+# these are matplotlib.patch.Patch properties
+props = dict(boxstyle='round', facecolor='wheat', alpha=0.75)
+# place a text box in upper left in axes coords
+a.text(0.15, 0.25, txtstr, transform=a.transAxes, fontsize=14,
+         verticalalignment='top', bbox=props)
+imgplot = plt.imshow(fgp_dTV_gpu3D[10,:,:], cmap="gray")
+plt.title('{}'.format('Recovered volume on the GPU using FGP-dTV'))
+#%%
diff --git a/demos/images/TV_vs_NLTV.jpg b/demos/images/TV_vs_NLTV.jpg
new file mode 100644
index 0000000..e976512
--- /dev/null
+++ b/demos/images/TV_vs_NLTV.jpg
diff --git a/demos/images/probl.pdf b/demos/images/probl.pdf
new file mode 100644
index 0000000..6a06021
--- /dev/null
+++ b/demos/images/probl.pdf
diff --git a/demos/images/probl.png b/demos/images/probl.png
new file mode 100644
index 0000000..af0e852
--- /dev/null
+++ b/demos/images/probl.png
diff --git a/demos/images/reg_penalties.jpg b/demos/images/reg_penalties.jpg
new file mode 100644
index 0000000..923d5c4
--- /dev/null
+++ b/demos/images/reg_penalties.jpg
diff --git a/demos/qualitymetrics.py b/demos/qualitymetrics.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/demos/qualitymetrics.py