diff options
Diffstat (limited to 'demos/demoMatlab_denoise.m')
| -rw-r--r-- | demos/demoMatlab_denoise.m | 16 |
1 files changed, 9 insertions, 7 deletions
diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m index 2031853..5135129 100644 --- a/demos/demoMatlab_denoise.m +++ b/demos/demoMatlab_denoise.m @@ -5,7 +5,9 @@ fsep = '/'; Path1 = sprintf(['..' fsep 'src' fsep 'Matlab' fsep 'mex_compile' fsep 'installed'], 1i); Path2 = sprintf([ data' fsep], 1i); Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i); -addpath(Path1); addpath(Path2); addpath(Path3); +addpath(Path1); +addpath(Path2); +addpath(Path3); Im = double(imread('lena_gray_512.tif'))/255; % loading image u0 = Im + .05*randn(size(Im)); u0(u0 < 0) = 0; @@ -29,7 +31,7 @@ figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)'); %% fprintf('Denoise using the FGP-TV model (CPU) \n'); -iter_fgp = 1000; % number of FGP iterations +iter_fgp = 1300; % number of FGP iterations epsil_tol = 1.0e-06; % tolerance tic; u_fgp = FGP_TV(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; energyfunc_val_fgp = TV_energy(single(u_fgp),single(u0),lambda_reg, 1); % get energy function value @@ -39,8 +41,8 @@ figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); %% % fprintf('Denoise using the FGP-TV model (GPU) \n'); -% iter_fgp = 1000; % number of FGP iterations -% epsil_tol = 1.0e-05; % tolerance +% iter_fgp = 1300; % number of FGP iterations +% epsil_tol = 1.0e-06; % tolerance % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_reg, iter_fgp, epsil_tol); toc; % figure; imshow(u_fgpG, [0 1]); title('FGP-TV denoised image (GPU)'); %% @@ -63,17 +65,17 @@ fprintf('Denoise using the TGV model (CPU) \n'); 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 +iter_TGV = 1500; % 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 = 0.045; % 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 +% iter_TGV = 1500; % 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); |