fixes all matlab issues

3 files changed, 428 insertions, 0 deletions

diff --git a/demos/Matlab_demos/demoMatlab_3Ddenoise.m b/demos/Matlab_demos/demoMatlab_3Ddenoise.m
new file mode 100644
index 0000000..d7ff60c
--- /dev/null
+++ b/demos/Matlab_demos/demoMatlab_3Ddenoise.m
@@ -0,0 +1,200 @@
+% Volume (3D) denoising demo using CCPi-RGL
+clear; close all
+fsep = '/';
+
+
+Path1 = sprintf(['..' fsep '..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i);
+Path2 = sprintf(['..' fsep 'data' fsep], 1i);
+Path3 = sprintf(['..' fsep '..' fsep 'src' fsep 'Matlab' fsep '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');
+%%
+fprintf('Denoise a volume using the ROF-TV model (CPU) \n');
+lambda_reg = 0.03; % regularsation parameter for all methods
+tau_rof = 0.0025; % time-marching constant 
+iter_rof = 300; % number of ROF iterations
+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);
+figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise a volume using the ROF-TV model (GPU) \n');
+% lambda_reg = 0.03; % regularsation parameter for all methods
+% tau_rof = 0.0025; % time-marching constant 
+% iter_rof = 300; % number of ROF iterations
+% epsil_tol =  0.0; % tolerance
+% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof, epsil_tol); 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');
+lambda_reg = 0.03; % regularsation parameter for all methods
+iter_fgp = 300; % number of FGP iterations
+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);
+figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)');
+%%
+fprintf('Denoise a volume using the FGP-TV model (GPU) \n');
+% lambda_reg = 0.03; % regularsation parameter for all methods
+% iter_fgp = 300; % number of FGP iterations
+% epsil_tol =  0.0; % 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 =  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);
+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 =  0.0; % 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 
+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)');
+%%
+% 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 
+% epsil_tol =  0.0; % tolerance
+% 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)');
+%%
+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 
+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)');
+%%
+% 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', 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)');
+%%
+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 
+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)');
+%%
+% 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, 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)');
+%%
+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
+L2 =  12.0; % convergence parameter
+iter_TGV = 500; % number of Primal-Dual iterations for TGV
+epsil_tol =  0.0; % tolerance
+tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, L2, 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)');
+%%
+% 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, L2, 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)');
+%%
+%>>>>>>>>>>>>>> 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 =  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)');
+%%
+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 =  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/Matlab_demos/demoMatlab_denoise.m b/demos/Matlab_demos/demoMatlab_denoise.m
new file mode 100644
index 0000000..5af927f
--- /dev/null
+++ b/demos/Matlab_demos/demoMatlab_denoise.m
@@ -0,0 +1,188 @@
+% Image (2D) denoising demo using CCPi-RGL
+clear; close all
+fsep = '/';
+
+Path1 = sprintf(['..' fsep '..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i);
+Path2 = sprintf(['..' fsep 'data' fsep], 1i);
+Path3 = sprintf(['..' fsep '..' fsep 'src' fsep 'Matlab' fsep 'supp'], 1i);
+addpath(Path1);
+addpath(Path2);
+addpath(Path3);
+
+Im = double(imread('lena_gray_512.tif'))/255;  % loading image
+u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0;
+figure; imshow(u0, [0 1]); title('Noisy image');
+%%
+fprintf('Denoise using the ROF-TV model (CPU) \n');
+lambda_reg = 0.03; % regularsation parameter for all methods
+iter_rof = 1500; % number of ROF iterations
+tau_rof = 0.003; % 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(:)));
+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');
+%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 = 0.03;
+iter_fgp = 500; % number of FGP iterations
+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(:)));
+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');
+% 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');
+lambda_reg = 0.03;
+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(:)));
+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');
+% 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 
+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);
+[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');
+%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 = 0.035; % regularisation parameter
+alpha1 = 1.0; % parameter to control the first-order term
+alpha0 = 2.0; % parameter to control the second-order term
+L2 =  12.0; % convergence parameter
+iter_TGV = 1200; % 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, L2, epsil_tol); toc; 
+figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
+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);
+%%
+% fprintf('Denoise using the TGV model (GPU) \n');
+% tic; u_tgv_gpu = TGV_GPU(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV, L2, 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 = 0.015; % 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);
+[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');
+% 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 = 3; % regularisation for the diffusivity 
+sigmaPar = 0.03; % edge-preserving parameter
+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);
+[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');
+%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)
+
+lambda_reg = 0.04;
+iter_fgp = 1000; % number of FGP iterations
+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(:)));
+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/Matlab_demos/demoMatlab_inpaint.m b/demos/Matlab_demos/demoMatlab_inpaint.m
new file mode 100644
index 0000000..67a6a23
--- /dev/null
+++ b/demos/Matlab_demos/demoMatlab_inpaint.m
@@ -0,0 +1,40 @@
+% Image (2D) inpainting demo using CCPi-RGL
+clear; close all
+
+fsep = '/';
+
+Path1 = sprintf(['..' fsep '..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i);
+Path2 = sprintf(['..' fsep 'data' fsep], 1i);
+Path3 = sprintf(['..' fsep '..' fsep 'src' fsep 'Matlab' fsep 'supp'], 1i);
+addpath(Path1);
+addpath(Path2);
+addpath(Path3);
+
+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