diff options
Diffstat (limited to 'Wrappers/Matlab/demos')
| -rw-r--r-- | Wrappers/Matlab/demos/demoMatlab_denoise.m | 42 |
1 files changed, 38 insertions, 4 deletions
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m index 8289f41..3f0ca54 100644 --- a/Wrappers/Matlab/demos/demoMatlab_denoise.m +++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m @@ -1,9 +1,11 @@ % Image (2D) denoising demo using CCPi-RGL clear; close all -Path1 = sprintf(['..' filesep 'mex_compile' filesep 'installed'], 1i); -Path2 = sprintf(['..' filesep '..' filesep '..' filesep 'data' filesep], 1i); -addpath(Path1); -addpath(Path2); +fsep = '/'; + +Path1 = sprintf(['..' fsep 'mex_compile' fsep 'installed'], 1i); +Path2 = sprintf(['..' fsep '..' fsep '..' fsep 'data' fsep], 1i); +Path3 = sprintf(['..' fsep 'supp'], 1i); +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; @@ -16,6 +18,8 @@ tau_rof = 0.0025; % time-marching constant iter_rof = 750; % number of ROF iterations tic; u_rof = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof); toc; energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1); % get energy function value +rmseROF = (RMSE(u_rof(:),Im(:))); +fprintf('%s %f \n', 'RMSE error for ROF-TV is:', rmseROF); figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)'); %% % fprintf('Denoise using the ROF-TV model (GPU) \n'); @@ -29,6 +33,8 @@ iter_fgp = 1000; % 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 +rmseFGP = (RMSE(u_fgp(:),Im(:))); +fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmseFGP); figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)'); %% @@ -43,6 +49,8 @@ iter_sb = 150; % number of SB iterations epsil_tol = 1.0e-06; % tolerance tic; u_sb = SB_TV(single(u0), lambda_reg, iter_sb, epsil_tol); toc; energyfunc_val_sb = TV_energy(single(u_sb),single(u0),lambda_reg, 1); % get energy function value +rmseSB = (RMSE(u_sb(:),Im(:))); +fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmseSB); figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)'); %% % fprintf('Denoise using the SB-TV model (GPU) \n'); @@ -51,12 +59,34 @@ figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)'); % tic; u_sbG = SB_TV_GPU(single(u0), lambda_reg, iter_sb, epsil_tol); toc; % 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 +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 +% 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); +% figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)'); +%% fprintf('Denoise using Nonlinear-Diffusion model (CPU) \n'); iter_diff = 800; % number of diffusion iterations 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(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +rmseDiffus = (RMSE(u_diff(:),Im(:))); +fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus); figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)'); %% % fprintf('Denoise using Nonlinear-Diffusion model (GPU) \n'); @@ -73,6 +103,8 @@ lambda_regDiff = 3.5; % regularisation for the diffusivity sigmaPar = 0.02; % edge-preserving parameter tau_param = 0.0015; % time-marching constant tic; u_diff4 = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; +rmseDiffHO = (RMSE(u_diff4(:),Im(:))); +fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rmseDiffHO); figure; imshow(u_diff4, [0 1]); title('Diffusion 4thO denoised image (CPU)'); %% % fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n'); @@ -95,6 +127,8 @@ iter_fgp = 1000; % number of FGP iterations epsil_tol = 1.0e-06; % tolerance eta = 0.2; % Reference image gradient smoothing constant tic; u_fgp_dtv = FGP_dTV(single(u0), single(u_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; +rmse_dTV= (RMSE(u_fgp_dtv(:),Im(:))); +fprintf('%s %f \n', 'RMSE error for Directional Total Variation (dTV) is:', rmse_dTV); figure; imshow(u_fgp_dtv, [0 1]); title('FGP-dTV denoised image (CPU)'); %% % fprintf('Denoise using the FGP-dTV model (GPU) \n'); |