2 files changed, 42 insertions, 10 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index c087433..9a65e37 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -57,23 +57,38 @@ 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
+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; 
 figure; imshow(u_diff(:,:,15), [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 = 0.06; % regularisation for the diffusivity 
-% sigmaPar = 0.04; % edge-preserving parameter
+% 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; 
 % figure; imshow(u_diff_g(:,:,15), [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; 
+figure; imshow(u_diff4(:,:,15), [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; 
+% figure; imshow(u_diff4_g(:,:,15), [0 1]); title('Diffusion 4thO denoised volume (GPU)');
+%%
 
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 fprintf('Denoise a volume using the FGP-dTV model (CPU) \n');
@@ -107,4 +122,4 @@ 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(:,:,15), [0 1]); title('FGP-dTV denoised volume (GPU)');
-%%
-\ No newline at end of file
+%%
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index d93f477..8289f41 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -53,20 +53,37 @@ figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
 %%
 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
+lambda_regDiff = 0.025; % regularisation for the diffusivity 
+sigmaPar = 0.015; % edge-preserving parameter
 tau_param = 0.025; % time-marching constant 
 tic; u_diff = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
 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
+% lambda_regDiff = 0.025; % regularisation for the diffusivity 
+% sigmaPar = 0.015; % edge-preserving parameter
 % tau_param = 0.025; % time-marching constant 
 % tic; u_diff_g = NonlDiff_GPU(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; 
 % figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)');
 %%
+fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n');
+iter_diff = 800; % number of diffusion iterations
+lambda_regDiff = 3.5; % regularisation for the diffusivity 
+sigmaPar = 0.02; % edge-preserving parameter
+tau_param = 0.0015; % time-marching constant 
+tic; u_diff4 = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; 
+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)');
+%%
+
 %>>>>>>>>>>>>>> MULTI-CHANNEL priors <<<<<<<<<<<<<<< %
 
 fprintf('Denoise using the FGP-dTV model (CPU) \n');