cmakelists fixes, matlab wrappers done

26 files changed, 106 insertions, 121 deletions

diff --git a/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
index 01491d9..ca8f1d2 100644
--- a/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
+++ b/demos/SoftwareX_supp/Demo_RealData_Recon_SX.py
@@ -111,7 +111,7 @@ RectoolsIR = RecToolsIR(DetectorsDimH =  np.size(det_y_crop),  # DetectorsDimH #
                     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
+                    tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier
                     device='gpu')
 #%%
 print ("Reconstructing with ADMM method using SB-TV penalty")
@@ -228,4 +228,4 @@ for i in range(0,np.size(RecADMM_reg_tgv,0)):
 # 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
index 59ffc0e..be99afe 100644
--- a/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
+++ b/demos/SoftwareX_supp/Demo_SimulData_ParOptimis_SX.py
@@ -77,7 +77,7 @@ RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector d
                     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
+                    tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier
                     device='gpu')
 #%%
 param_space = 30
diff --git a/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py b/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
index 99b9fe8..ae2bfba 100644
--- a/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
+++ b/demos/SoftwareX_supp/Demo_SimulData_Recon_SX.py
@@ -78,7 +78,6 @@ 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()
 #%%
@@ -164,7 +163,7 @@ RectoolsIR = RecToolsIR(DetectorsDimH = Horiz_det,  # DetectorsDimH # detector d
                     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 inner iterations earlier
+                    tolerance = 0.0, # tolerance to stop inner (regularisation) iterations earlier
                     device='gpu')
 #%%
 print ("Reconstructing with ADMM method using SB-TV penalty")
diff --git a/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m
index cf2c88a..ec0fd88 100644
--- a/demos/demoMatlab_3Ddenoise.m
+++ b/demos/demoMatlab_3Ddenoise.m
@@ -23,7 +23,8 @@ 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; 
+epsil_tol =  0.0; % tolerance
+tic; [u_rof,infovec] = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof, epsil_tol); 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);
@@ -39,8 +40,8 @@ figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)');
 %%
 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; 
+epsil_tol =  0.0; % tolerance
+tic; [u_fgp,infovec] = 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);
@@ -56,8 +57,8 @@ figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)');
 %%
 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; 
+epsil_tol =  0.0; % tolerance
+tic; [u_sb,infovec] = 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);
@@ -76,7 +77,8 @@ 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; 
+epsil_tol =  0.0; % tolerance
+tic; [u_rof_llt, infovec] = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); 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)');
@@ -86,7 +88,7 @@ figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)');
 % 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; 
+% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); 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)');
@@ -96,7 +98,8 @@ 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; 
+epsil_tol =  0.0; % tolerance
+tic; [u_diff, infovec]  = NonlDiff(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); 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)');
@@ -106,7 +109,7 @@ figure; imshow(u_diff(:,:,7), [0 1]); title('Diffusion denoised volume (CPU)');
 % 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; 
+% tic; u_diff_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); 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)');
@@ -116,7 +119,8 @@ 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; 
+epsil_tol =  0.0; % tolerance
+tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); 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)');
@@ -126,7 +130,7 @@ figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CP
 % 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; 
+% tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); 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)');
@@ -136,7 +140,8 @@ 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; 
+epsil_tol =  0.0; % tolerance
+tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); 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)');
@@ -146,7 +151,7 @@ figure; imshow(u_tgv(:,:,3), [0 1]); title('TGV denoised volume (CPU)');
 % 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; 
+% tic; u_tgv_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); 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)');
@@ -163,7 +168,7 @@ 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
+epsil_tol =  0.0; % 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)');
@@ -179,7 +184,7 @@ 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
+epsil_tol =  0.0; % 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
index 7581068..377a447 100644
--- a/demos/demoMatlab_denoise.m
+++ b/demos/demoMatlab_denoise.m
@@ -14,10 +14,10 @@ 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 = 0.02; % regularsation parameter for all methods
+lambda_reg = 0.03; % regularsation parameter for all methods
 iter_rof = 2000; % number of ROF iterations
-tau_rof = 0.001; % time-marching constant 
-epsil_tol =  0.0; % tolerance
+tau_rof = 0.01; % time-marching constant 
+epsil_tol =  0.0; % tolerance / 1.0e-06
 tic; [u_rof,infovec] = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; 
 energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1);  % get energy function value
 rmseROF = (RMSE(u_rof(:),Im(:)));
@@ -26,14 +26,14 @@ fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF);
 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');
-% tic; [u_rofG,infovec]  = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; 
-% figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
+%fprintf('Denoise using the ROF-TV model (GPU) \n');
+%tic; [u_rofG,infovec]  = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc; 
+%figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
 %%
 fprintf('Denoise using the FGP-TV model (CPU) \n');
-lambda_reg = 0.02;
+lambda_reg = 0.03;
 iter_fgp = 500; % number of FGP iterations
-epsil_tol =  1.0e-06; % tolerance
+epsil_tol =  0.0; % tolerance
 tic; [u_fgp,infovec] = 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(:)));
@@ -48,8 +48,8 @@ figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
 %%
 fprintf('Denoise using the SB-TV model (CPU) \n');
 lambda_reg = 0.03;
-iter_sb = 300; % number of SB iterations
-epsil_tol =  1.0e-06; % tolerance
+iter_sb = 200; % number of SB iterations
+epsil_tol =  0.0; % tolerance
 tic; [u_sb,infovec] = 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(:)));
@@ -67,7 +67,7 @@ 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 
-epsil_tol =  1.0e-06; % tolerance
+epsil_tol =  0.0; % tolerance
 tic; [u_diff,infovec] = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); toc; 
 rmseDiffus = (RMSE(u_diff(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus);
@@ -75,20 +75,17 @@ fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus);
 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 Nonlinear-Diffusion model (GPU) \n');
+%tic; u_diff_g = NonlDiff_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); toc; 
+%figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)');
 %%
 fprintf('Denoise using the TGV model (CPU) \n');
-lambda_TGV = 0.045; % regularisation parameter
+lambda_TGV = 0.035; % regularisation parameter
 alpha1 = 1.0; % parameter to control the first-order term
 alpha0 = 2.0; % parameter to control the second-order term
-iter_TGV = 2500; % number of Primal-Dual iterations for TGV
-tic; [u_tgv,infovec] = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
+iter_TGV = 20; % number of Primal-Dual iterations for TGV
+epsil_tol =  0.0; % tolerance
+tic; [u_tgv,infovec] = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; 
 rmseTGV = (RMSE(u_tgv(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
 [ssimval] = ssim(u_tgv*255,single(Im)*255);
@@ -96,23 +93,15 @@ 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);
+% tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, epsil_tol); toc; 
 % figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)');
 %%
 fprintf('Denoise using the ROF-LLT model (CPU) \n');
 lambda_ROF = 0.02; % ROF regularisation parameter
-lambda_LLT = 0.01; % LLT regularisation parameter
-iter_LLT = 1000; % iterations 
-tau_rof_llt = 0.0025; % time-marching constant 
-epsil_tol =  1.0e-06; % tolerance
+lambda_LLT = 0.015; % LLT regularisation parameter
+iter_LLT = 2000; % iterations 
+tau_rof_llt = 0.01; % time-marching constant 
+epsil_tol = 0.0; % tolerance
 tic; [u_rof_llt,infovec]  = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt,epsil_tol); toc; 
 rmseROFLLT = (RMSE(u_rof_llt(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT);
@@ -121,21 +110,15 @@ 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);
+% tic; u_rof_llt_g = LLT_ROF_GPU(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); toc; 
 % 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 = 2.5; % regularisation for the diffusivity 
+lambda_regDiff = 3; % regularisation for the diffusivity 
 sigmaPar = 0.03; % edge-preserving parameter
-tau_param = 0.0015; % time-marching constant 
-epsil_tol =  1.0e-06; % tolerance
+tau_param = 0.0025; % time-marching constant 
+epsil_tol =  0.0; % tolerance
 tic; [u_diff4,infovec] = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); toc; 
 rmseDiffHO = (RMSE(u_diff4(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rmseDiffHO);
@@ -143,13 +126,9 @@ fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rms
 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('Denoise using Fourth-order anisotropic diffusion model (GPU) \n');
+%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;
@@ -177,7 +156,7 @@ u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0;
 
 lambda_reg = 0.04;
 iter_fgp = 1000; % number of FGP iterations
-epsil_tol =  1.0e-06; % tolerance
+epsil_tol =  0.0; % tolerance
 eta =  0.2; % Reference image gradient smoothing constant
 tic; [u_fgp_dtv,infovec] = FGP_dTV(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; 
 rmse_dTV= (RMSE(u_fgp_dtv(:),Im(:)));
diff --git a/run.sh b/run.sh
index b2be0d6..7700603 100755
--- a/run.sh
+++ b/run.sh
@@ -10,17 +10,17 @@ export CIL_VERSION=19.03
 # install Python modules without CUDA
 # cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
 # install Python modules with CUDA
-cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
+# cmake ../ -DBUILD_PYTHON_WRAPPER=ON -DBUILD_MATLAB_WRAPPER=OFF -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
 # install Matlab modules without CUDA
-# cmake ../ -DBUILD_PYTHON_WRAPPER=OFF -DMatlab_ROOT_DIR=/dls_sw/apps/matlab/r2014a/ -DBUILD_MATLAB_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
+#cmake ../ -DBUILD_PYTHON_WRAPPER=OFF -DMatlab_ROOT_DIR=/dls_sw/apps/matlab/r2014a/ -DBUILD_MATLAB_WRAPPER=ON -DBUILD_CUDA=OFF -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
 # install Matlab modules with CUDA
-# cmake ../ -DBUILD_PYTHON_WRAPPER=OFF -DMatlab_ROOT_DIR=/dls_sw/apps/matlab/r2014a/ -DBUILD_MATLAB_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
+cmake ../ -DBUILD_PYTHON_WRAPPER=OFF -DMatlab_ROOT_DIR=/dls_sw/apps/matlab/r2014a/ -DBUILD_MATLAB_WRAPPER=ON -DBUILD_CUDA=ON -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=./install
 make install
 #### Python
 #cp install/lib/libcilreg.so install/python/ccpi/filters
 # cd install/python
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../lib
-spyder
+# export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:../lib
+# spyder
 ##### Matlab (Linux)
 #PATH="/path/to/mex/:$PATH" LD_LIBRARY_PATH="/path/to/library:$LD_LIBRARY_PATH" matlab
-#PATH="/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/build_proj/install/matlab/:$PATH" LD_LIBRARY_PATH="/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/build_proj/install/lib:$LD_LIBRARY_PATH" matlab
+PATH="/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/build_proj/install/matlab/:$PATH" LD_LIBRARY_PATH="/home/kjy41806/Documents/SOFT/CCPi-Regularisation-Toolkit/build_proj/install/lib:$LD_LIBRARY_PATH" matlab
diff --git a/src/Matlab/CMakeLists.txt b/src/Matlab/CMakeLists.txt
index 6c5e6be..0897d7a 100755
--- a/src/Matlab/CMakeLists.txt
+++ b/src/Matlab/CMakeLists.txt
@@ -85,10 +85,10 @@ foreach(tgt RANGE 0 ${num})
     )

     

 target_include_directories(${current_target}

-   PUBLIC ${CMAKE_SOURCE_DIR}/Core/regularisers_CPU

-   ${CMAKE_SOURCE_DIR}/Core/regularisers_GPU

-   ${CMAKE_SOURCE_DIR}/Core/inpainters_CPU

-   ${CMAKE_SOURCE_DIR}/Core/

+   PUBLIC ${CMAKE_SOURCE_DIR}/src/Core/regularisers_CPU

+   ${CMAKE_SOURCE_DIR}/src/Core/regularisers_GPU

+   ${CMAKE_SOURCE_DIR}/src/Core/inpainters_CPU

+   ${CMAKE_SOURCE_DIR}/src/Core/

    ${MATLAB_INCLUDE_DIR})

    set_property(TARGET ${current_target} PROPERTY C_STANDARD 99)

    list(APPEND CPU_MEX_TARGETS ${current_target})

@@ -131,14 +131,16 @@ message("number of GPU files  " ${num})
           )

         

         target_include_directories(${current_target}

-        PUBLIC ${CMAKE_SOURCE_DIR}/Core/regularisers_CPU

-               ${CMAKE_SOURCE_DIR}/Core/regularisers_GPU

-               ${CMAKE_SOURCE_DIR}/Core/inpainters_CPU

-               ${CMAKE_SOURCE_DIR}/Core/

+        PUBLIC ${CMAKE_SOURCE_DIR}/src/Core/regularisers_CPU

+               ${CMAKE_SOURCE_DIR}/src/Core/regularisers_GPU

+               ${CMAKE_SOURCE_DIR}/src/Core/inpainters_CPU

+               ${CMAKE_SOURCE_DIR}/src/Core/

                ${MATLAB_INCLUDE_DIR})

        

         list(APPEND GPU_MEX_TARGETS ${current_target})

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

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

       endforeach()

        

       add_custom_target(MatlabWrapperGPU DEPENDS ${GPU_MEX_TARGETS})

diff --git a/src/Matlab/mex_compile/installed/MEXed_files_location.txt b/src/Matlab/mex_compile/installed/MEXed_files_location.txt
deleted file mode 100644
index e69de29..0000000
--- a/src/Matlab/mex_compile/installed/MEXed_files_location.txt
+++ /dev/null
diff --git a/src/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c b/src/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c
index a003596..887a76d 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/Diffusion_4thO.c
@@ -78,9 +78,9 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));    
     
     Diffus4th_CPU_main(Input, Output, infovec, lambda, sigma, iter_numb, tau, epsil, dimX, dimY, dimZ);
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/FGP_TV.c b/src/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
index f6db6c8..251ac52 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/FGP_TV.c
@@ -89,7 +89,7 @@ void mexFunction(
     if (number_of_dims == 3) {    
         Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     }    
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
     
diff --git a/src/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c b/src/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c
index 3122610..f1b70a8 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/FGP_dTV.c
@@ -101,10 +101,10 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
     
     /* running the function */
     dTV_FGP_CPU_main(Input, InputRef, Output, infovec, lambda, iter, epsil, eta, methTV, nonneg, dimX, dimY, dimZ);
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c b/src/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
index f630397..5c6de9d 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/LLT_ROF.c
@@ -83,9 +83,9 @@ void mexFunction(
     }    
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));   
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));   
   
     LLT_ROF_CPU_main(Input, Output, infovec, lambdaROF, lambdaLLT, iterationsNumb, tau, epsil, dimX, dimY, dimZ);    
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/NonlDiff.c b/src/Matlab/mex_compile/regularisers_CPU/NonlDiff.c
index 57c8811..2ca17d2 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/NonlDiff.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/NonlDiff.c
@@ -90,9 +90,9 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));    
     
     Diffusion_CPU_main(Input, Output, infovec, lambda, sigma, iter_numb, tau, penaltytype, epsil, dimX, dimY, dimZ);
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c b/src/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
index 014c0a0..34b9915 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/Nonlocal_TV.c
@@ -51,8 +51,8 @@ void mexFunction(
     long number_of_dims,  dimX, dimY, dimZ;
     int IterNumb, NumNeighb = 0;
     unsigned short *H_i, *H_j, *H_k;
-    const int  *dim_array;
-    const int  *dim_array2;
+    const mwSize  *dim_array;
+    const mwSize *dim_array2;
     float *A_orig, *Output=NULL, *Weights, lambda;
     
     dim_array = mxGetDimensions(prhs[0]);
diff --git a/src/Matlab/mex_compile/regularisers_CPU/PatchSelect.c b/src/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
index f942539..d2f6670 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/PatchSelect.c
@@ -53,10 +53,10 @@ void mexFunction(
     int number_of_dims,  SearchWindow, SimilarWin, NumNeighb;
     mwSize dimX, dimY, dimZ;
     unsigned short *H_i=NULL, *H_j=NULL, *H_k=NULL;
-    const int  *dim_array;
+    mwSize  *dim_array;
     float *A, *Weights = NULL, h;
-    int dim_array2[3]; /* for 2D data */
-    int dim_array3[4]; /* for 3D data */
+    mwSize dim_array2[3]; /* for 2D data */
+    mwSize dim_array3[4]; /* for 3D data */
     
     dim_array = mxGetDimensions(prhs[0]);
     number_of_dims = mxGetNumberOfDimensions(prhs[0]);
diff --git a/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c b/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
index a7d431f..ffe7b91 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/ROF_TV.c
@@ -77,9 +77,9 @@ void mexFunction(
         Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array_i, mxSINGLE_CLASS, mxREAL));
     }
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
      
     TV_ROF_CPU_main(Input, Output, infovec, lambda, iter_numb, tau, epsil, dimX, dimY, dimZ);    
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_CPU/SB_TV.c b/src/Matlab/mex_compile/regularisers_CPU/SB_TV.c
index 495f1c9..d1bdb3a 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/SB_TV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/SB_TV.c
@@ -82,7 +82,7 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));    
     
diff --git a/src/Matlab/mex_compile/regularisers_CPU/TGV.c b/src/Matlab/mex_compile/regularisers_CPU/TGV.c
index aab01b4..2c0fcbd 100644
--- a/src/Matlab/mex_compile/regularisers_CPU/TGV.c
+++ b/src/Matlab/mex_compile/regularisers_CPU/TGV.c
@@ -57,7 +57,7 @@ void mexFunction(
     dim_array = mxGetDimensions(prhs[0]);
     
     /*Handling Matlab input data*/
-    if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
+    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
     
     Input  = (float *) mxGetData(prhs[0]); /*noisy image/volume */
     lambda =  (float) mxGetScalar(prhs[1]); /* regularisation parameter */
@@ -85,7 +85,7 @@ void mexFunction(
         Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     }    
  
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));    
     
diff --git a/src/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp
index 7b7a220..42874ef 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/Diffusion_4thO_GPU.cpp
@@ -78,9 +78,9 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));    
     
     Diffus4th_GPU_main(Input, Output, infovec, lambda, sigma, iter_numb, tau, epsil, dimX, dimY, dimZ);
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
index 5ccc2b2..d08e50d 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/FGP_TV_GPU.cpp
@@ -89,10 +89,10 @@ void mexFunction(
     if (number_of_dims == 3) {    
         Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     }    
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
     
     /* running the function */
     TV_FGP_GPU_main(Input, Output, infovec, lambda, iter, epsil, methTV, nonneg, dimX, dimY, dimZ);    
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp
index 6662e0b..2db4556 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/FGP_dTV_GPU.cpp
@@ -101,10 +101,10 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
     
     /* running the function */
     dTV_FGP_GPU_main(Input, InputRef, Output, infovec, lambda, iter, epsil, eta, methTV, nonneg, dimX, dimY, dimZ);
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
index f27767e..ff5d577 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/LLT_ROF_GPU.cpp
@@ -83,9 +83,9 @@ void mexFunction(
     }    
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));   
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));   
   
     LLT_ROF_GPU_main(Input, Output, infovec, lambdaROF, lambdaLLT, iterationsNumb, tau, epsil, dimX, dimY, dimZ);    
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp
index 4ce983f..43627c8 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/NonlDiff_GPU.cpp
@@ -92,9 +92,9 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));   
     
     NonlDiff_GPU_main(Input, Output, infovec, lambda, sigma, iter_numb, tau, penaltytype, epsil, dimX, dimY, dimZ);
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
index 4172323..d9b7e83 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/ROF_TV_GPU.cpp
@@ -75,9 +75,9 @@ void mexFunction(
         Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array_i, mxSINGLE_CLASS, mxREAL));
     }
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));
     
     TV_ROF_GPU_main(Input, Output, infovec, lambda, iter_numb, tau, epsil, dimX, dimY, dimZ);    
-}
\ No newline at end of file
+}
diff --git a/src/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp
index 8ec95ab..562dc65 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/SB_TV_GPU.cpp
@@ -72,7 +72,7 @@ void mexFunction(
     }
     if (number_of_dims == 3) Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));    
     
diff --git a/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp b/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
index bdfd85b..eb1f043 100644
--- a/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
+++ b/src/Matlab/mex_compile/regularisers_GPU/TGV_GPU.cpp
@@ -60,7 +60,7 @@ void mexFunction(
     dim_array = mxGetDimensions(prhs[0]);
     
     /*Handling Matlab input data*/
-    if ((nrhs < 2) || (nrhs > 6)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
+    if ((nrhs < 2) || (nrhs > 7)) mexErrMsgTxt("At least 2 parameters is required, all parameters are: Image(2D), Regularisation parameter, alpha0, alpha1, iterations number, Lipshitz Constant");
     
     Input  = (float *) mxGetData(prhs[0]); /*noisy image/volume */
     lambda =  (float) mxGetScalar(prhs[1]); /* regularisation parameter */
@@ -88,7 +88,7 @@ void mexFunction(
         Output = (float*)mxGetPr(plhs[0] = mxCreateNumericArray(3, dim_array, mxSINGLE_CLASS, mxREAL));
     }    
  
-    int vecdim[1];
+    mwSize vecdim[1];
     vecdim[0] = 2;
     infovec = (float*)mxGetPr(plhs[1] = mxCreateNumericArray(1, vecdim, mxSINGLE_CLASS, mxREAL));