diff options
Diffstat (limited to 'Wrappers/Matlab')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 14 | ||||
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 4 | ||||
| -rw-r--r-- | Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt | 0 |
3 files changed, 9 insertions, 9 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m index f5c3ad1..71082e7 100644 --- a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m @@ -3,10 +3,10 @@ addpath('../mex_compile/installed'); addpath('../../../data/'); -N = 256; +N = 512; slices = 30; vol3D = zeros(N,N,slices, 'single'); -Im = double(imread('lena_gray_256.tif'))/255; % loading image +Im = double(imread('lena_gray_512.tif'))/255; % loading image for i = 1:slices vol3D(:,:,i) = Im + .05*randn(size(Im)); end @@ -17,28 +17,28 @@ figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image'); fprintf('Denoise using ROF-TV model (CPU) \n'); lambda_rof = 0.03; % regularisation parameter tau_rof = 0.0025; % time-marching constant -iter_rof = 1000; % number of ROF iterations +iter_rof = 300; % number of ROF iterations tic; u_rof = ROF_TV(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)'); %% % fprintf('Denoise using ROF-TV model (GPU) \n'); % lambda_rof = 0.03; % regularisation parameter % tau_rof = 0.0025; % time-marching constant -% iter_rof = 1000; % number of ROF iterations +% iter_rof = 300; % number of ROF iterations % tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; % figure; imshow(u_rofG(:,:,15), [0 1]); title('ROF-TV denoised volume (GPU)'); %% fprintf('Denoise using FGP-TV model (CPU) \n'); lambda_fgp = 0.03; % regularisation parameter -iter_fgp = 500; % number of FGP iterations +iter_fgp = 300; % number of FGP iterations epsil_tol = 1.0e-05; % tolerance tic; u_fgp = FGP_TV(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)'); %% % fprintf('Denoise using FGP-TV model (GPU) \n'); % lambda_fgp = 0.03; % regularisation parameter -% iter_fgp = 500; % number of FGP iterations +% iter_fgp = 300; % number of FGP iterations % epsil_tol = 1.0e-05; % tolerance % tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG(:,:,15), [0 1]); title('FGP-TV 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 ab4e95d..7f87fbb 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -3,7 +3,7 @@ addpath('../mex_compile/installed'); addpath('../../../data/'); -Im = double(imread('lena_gray_256.tif'))/255; % loading image +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'); @@ -35,4 +35,4 @@ figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); % epsil_tol = 1.0e-05; % tolerance % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)'); -%%
\ No newline at end of file +%% diff --git a/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt b/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt new file mode 100644 index 0000000..e69de29 --- /dev/null +++ b/Wrappers/Matlab/mex_compile/installed/MEXed_files_location.txt |