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| author | Daniil Kazantsev <dkazanc3@googlemail.com> | 2018-04-09 09:43:34 +0100 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-04-09 09:43:34 +0100 |
| commit | a5b5872b76bf00023a7e7cee97e028003ccbc45e (patch) | |
| tree | 733de21b6cc28d89e531bc1259b7113b62007fb1 /Wrappers/Matlab/demos | |
| parent | ef20eb1e81775a4767b8d9d25ad8081d3870c167 (diff) | |
| download | regularization-a5b5872b76bf00023a7e7cee97e028003ccbc45e.tar.gz regularization-a5b5872b76bf00023a7e7cee97e028003ccbc45e.tar.bz2 regularization-a5b5872b76bf00023a7e7cee97e028003ccbc45e.tar.xz regularization-a5b5872b76bf00023a7e7cee97e028003ccbc45e.zip | |
Delete demoMatlab_denoise.m~
deleted redundant *m file
Diffstat (limited to 'Wrappers/Matlab/demos')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m~ | 31 |
1 files changed, 0 insertions, 31 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m~ b/Wrappers/Matlab/demos/demoMatlab_denoise.m~ deleted file mode 100644 index 3f4403e..0000000 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m~ +++ /dev/null @@ -1,31 +0,0 @@ -% Image (2D) denoising demo using CCPi-RGL - -addpath('../mex_compile/installed'); -addpath('../../../data/'); - -Im = double(imread('lena_gray_256.tif'))/255; % loading image -u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; -figure; imshow(u0, [0 1]); title('Noisy image'); - -%% -fprintf('Denoise using ROF-TV model (CPU) \n'); -lambda_rof = 0.02; % regularization parameter -tau_rof = 0.0025; % time-marching constant -iter_rof = 2000; % number of ROF iterations -tic; u_rof = ROF_TV(single(u0), lambda_rof, iter_rof, tau_rof); toc; -figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); -%% -% fprintf('Denoise using ROF-TV model (GPU) \n'); -% lambda_rof = 0.02; % regularization parameter -% tau_rof = 0.0025; % time-marching constant -% iter_rof = 2000; % number of ROF iterations -% tic; u_rof = ROF_TV_GPU(single(u0), lambda_rof, iter_rof, tau_rof); toc; -% figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (GPU)'); -%% -fprintf('Denoise using FGP-TV model (CPU) \n'); -lambda_fgp = 0.02; % regularization parameter -iter_fgp = 2000; % number of FGP iterations -epsil_tol = 1.0e-05; % tolerance -tic; u_fgp = FGP_TV(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; -figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); -%%
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