2 files changed, 25 insertions, 0 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index fb55097..502b6bd 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -53,6 +53,15 @@ figure; imshow(u_sb(:,:,15), [0 1]); title('SB-TV denoised volume (CPU)');
 % tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; 
 % figure; imshow(u_sbG(:,:,15), [0 1]); title('SB-TV denoised volume (GPU)');
 %%
+%%
+fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n');
+iter_diff = 300; % number of diffusion iterations
+lambda_regDiff = 0.06; % regularisation for the diffusivity 
+sigmaPar = 0.04; % 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; 
+figure; imshow(u_diff(:,:,15), [0 1]); title('Diffusion denoised volume (CPU)');
+%%
 
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 fprintf('Denoise a volume using the FGP-dTV model (CPU) \n');
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index dab98dc..4a0a19a 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -46,6 +46,22 @@ figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
 % 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 = 800; % number of diffusion iterations
+lambda_regDiff = 0.06; % regularisation for the diffusivity 
+sigmaPar = 0.04; % edge-preserving parameter
+tau_param = 0.025; % time-marching constant 
+tic; u_diff = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
+figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)');
+%%
+% fprintf('Denoise using Nonlinear-Diffusion model (GPU) \n');
+% iter_diff = 800; % number of diffusion iterations
+% lambda_regDiff = 0.06; % regularisation for the diffusivity 
+% sigmaPar = 0.04; % 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)');
+%%
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 
 fprintf('Denoise using the FGP-dTV model (CPU) \n');