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authorDaniil Kazantsev <dkazanc@hotmail.com>2018-05-04 09:57:09 +0100
committerDaniil Kazantsev <dkazanc@hotmail.com>2018-05-04 09:57:09 +0100
commitfd62af62943acf481960eebbe9e986a620e6ebc9 (patch)
tree91c025c65bf810c86d0eb918b610306f0924f35d /Wrappers/Matlab
parent824fafc9d39bfd3a27ffdb29c37f873e6097c3f7 (diff)
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corrections to demos
Diffstat (limited to 'Wrappers/Matlab')
-rw-r--r--Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m9
-rw-r--r--Wrappers/Matlab/demos/demoMatlab_denoise.m4
2 files changed, 6 insertions, 7 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index 33dfb95..9a65e37 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -57,19 +57,18 @@ 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)');
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index df47bdc..8289f41 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -61,8 +61,8 @@ 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)');