Merge pull request #98 from vais-ral/TGV3D

TGV 3D CPU/GPU

2 files changed, 21 insertions, 10 deletions

diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
index 5cc47b3..0c331a4 100644
--- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -2,11 +2,13 @@
 clear; close all
 Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i);
 Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i);
+Path3 = sprintf(['..' filesep 'supp'], 1i);
 addpath(Path1);
 addpath(Path2);
+addpath(Path3);
 
 N = 512; 
-slices = 15;
+slices = 7;
 vol3D = zeros(N,N,slices, 'single');
 Ideal3D = zeros(N,N,slices, 'single');
 Im = double(imread('lena_gray_512.tif'))/255;  % loading image
@@ -131,7 +133,16 @@ figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CP
 % 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
+iter_TGV = 500; % number of Primal-Dual iterations for TGV
+tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV); 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)');
+%%
 %>>>>>>>>>>>>>> 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 3506cca..14d3096 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -60,20 +60,20 @@ figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
 % figure; imshow(u_sbG, [0 1]); title('SB-TV denoised image (GPU)');
 %%
 fprintf('Denoise using the TGV model (CPU) \n');
-lambda_TGV = 0.04; % regularisation parameter
-alpha1 = 1; % parameter to control the first-order term
-alpha0 = 0.7; % parameter to control the second-order term
-iter_TGV = 500; % number of Primal-Dual iterations for TGV
+lambda_TGV = 0.045; % regularisation parameter
+alpha1 = 1.0; % parameter to control the first-order term
+alpha0 = 2.0; % parameter to control the second-order term
+iter_TGV = 2000; % number of Primal-Dual iterations for TGV
 tic; u_tgv = TGV(single(u0), lambda_TGV, alpha1, alpha0, iter_TGV); toc; 
 rmseTGV = (RMSE(u_tgv(:),Im(:)));
 fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
 figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
 %%
 % fprintf('Denoise using the TGV model (GPU) \n');
-% lambda_TGV = 0.04; % regularisation parameter
-% alpha1 = 1; % parameter to control the first-order term
-% alpha0 = 0.7; % parameter to control the second-order term
-% iter_TGV = 500; % number of Primal-Dual iterations for TGV
+% lambda_TGV = 0.045; % regularisation parameter
+% alpha1 = 1.0; % parameter to control the first-order term
+% alpha0 = 2.0; % parameter to control the second-order term
+% iter_TGV = 2000; % 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);