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Diffstat (limited to 'demos/demoMatlab_3Ddenoise.m')
| -rw-r--r-- | demos/demoMatlab_3Ddenoise.m | 178 |
1 files changed, 178 insertions, 0 deletions
diff --git a/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m new file mode 100644 index 0000000..cdd3117 --- /dev/null +++ b/demos/demoMatlab_3Ddenoise.m @@ -0,0 +1,178 @@ +% Volume (3D) denoising demo using CCPi-RGL +clear; close all +Path1 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'mex_compile' filesep 'installed'], 1i); +Path2 = sprintf(['data' filesep], 1i); +Path3 = sprintf(['..' filesep 'src' filesep 'Matlab' filesep 'supp'], 1i); +addpath(Path1); +addpath(Path2); +addpath(Path3); + +N = 512; +slices = 7; +vol3D = zeros(N,N,slices, 'single'); +Ideal3D = zeros(N,N,slices, 'single'); +Im = double(imread('lena_gray_512.tif'))/255; % loading image +for i = 1:slices +vol3D(:,:,i) = Im + .05*randn(size(Im)); +Ideal3D(:,:,i) = Im; +end +vol3D(vol3D < 0) = 0; +figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image'); + + +lambda_reg = 0.03; % regularsation parameter for all methods +%% +fprintf('Denoise a volume using the ROF-TV model (CPU) \n'); +tau_rof = 0.0025; % time-marching constant +iter_rof = 300; % number of ROF iterations +tic; u_rof = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; +energyfunc_val_rof = TV_energy(single(u_rof),single(vol3D),lambda_reg, 1); % get energy function value +rmse_rof = (RMSE(Ideal3D(:),u_rof(:))); +fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rof); +figure; imshow(u_rof(:,:,7), [0 1]); title('ROF-TV denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using the ROF-TV model (GPU) \n'); +% tau_rof = 0.0025; % time-marching constant +% iter_rof = 300; % number of ROF iterations +% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof); toc; +% rmse_rofG = (RMSE(Ideal3D(:),u_rofG(:))); +% fprintf('%s %f \n', 'RMSE error for ROF is:', rmse_rofG); +% figure; imshow(u_rofG(:,:,7), [0 1]); title('ROF-TV denoised volume (GPU)'); +%% +fprintf('Denoise a volume using the FGP-TV model (CPU) \n'); +iter_fgp = 300; % number of FGP iterations +epsil_tol = 1.0e-05; % tolerance +tic; u_fgp = FGP_TV(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; +energyfunc_val_fgp = TV_energy(single(u_fgp),single(vol3D),lambda_reg, 1); % get energy function value +rmse_fgp = (RMSE(Ideal3D(:),u_fgp(:))); +fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgp); +figure; imshow(u_fgp(:,:,7), [0 1]); title('FGP-TV denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using the FGP-TV model (GPU) \n'); +% iter_fgp = 300; % number of FGP iterations +% epsil_tol = 1.0e-05; % tolerance +% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_reg, iter_fgp, epsil_tol); toc; +% rmse_fgpG = (RMSE(Ideal3D(:),u_fgpG(:))); +% fprintf('%s %f \n', 'RMSE error for FGP-TV is:', rmse_fgpG); +% figure; imshow(u_fgpG(:,:,7), [0 1]); title('FGP-TV denoised volume (GPU)'); +%% +fprintf('Denoise a volume using the SB-TV model (CPU) \n'); +iter_sb = 150; % number of SB iterations +epsil_tol = 1.0e-05; % tolerance +tic; u_sb = SB_TV(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; +energyfunc_val_sb = TV_energy(single(u_sb),single(vol3D),lambda_reg, 1); % get energy function value +rmse_sb = (RMSE(Ideal3D(:),u_sb(:))); +fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sb); +figure; imshow(u_sb(:,:,7), [0 1]); title('SB-TV denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using the SB-TV model (GPU) \n'); +% iter_sb = 150; % number of SB iterations +% epsil_tol = 1.0e-05; % tolerance +% tic; u_sbG = SB_TV_GPU(single(vol3D), lambda_reg, iter_sb, epsil_tol); toc; +% rmse_sbG = (RMSE(Ideal3D(:),u_sbG(:))); +% fprintf('%s %f \n', 'RMSE error for SB-TV is:', rmse_sbG); +% figure; imshow(u_sbG(:,:,7), [0 1]); title('SB-TV denoised volume (GPU)'); +%% +fprintf('Denoise a volume using the ROF-LLT model (CPU) \n'); +lambda_ROF = lambda_reg; % ROF regularisation parameter +lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter +iter_LLT = 300; % iterations +tau_rof_llt = 0.0025; % time-marching constant +tic; u_rof_llt = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; +rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt(:))); +fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt); +figure; imshow(u_rof_llt(:,:,7), [0 1]); title('ROF-LLT denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using the ROF-LLT model (GPU) \n'); +% lambda_ROF = lambda_reg; % ROF regularisation parameter +% lambda_LLT = lambda_reg*0.35; % LLT regularisation parameter +% iter_LLT = 300; % iterations +% tau_rof_llt = 0.0025; % time-marching constant +% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc; +% rmse_rof_llt = (RMSE(Ideal3D(:),u_rof_llt_g(:))); +% fprintf('%s %f \n', 'RMSE error for ROF-LLT is:', rmse_rof_llt); +% figure; imshow(u_rof_llt_g(:,:,7), [0 1]); title('ROF-LLT denoised volume (GPU)'); +%% +fprintf('Denoise a volume using Nonlinear-Diffusion model (CPU) \n'); +iter_diff = 300; % 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(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +rmse_diff = (RMSE(Ideal3D(:),u_diff(:))); +fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff); +figure; imshow(u_diff(:,:,7), [0 1]); title('Diffusion denoised volume (CPU)'); +%% +% fprintf('Denoise a volume using Nonlinear-Diffusion model (GPU) \n'); +% iter_diff = 300; % 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_g = NonlDiff_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc; +% rmse_diff = (RMSE(Ideal3D(:),u_diff_g(:))); +% fprintf('%s %f \n', 'RMSE error for Diffusion is:', rmse_diff); +% figure; imshow(u_diff_g(:,:,7), [0 1]); title('Diffusion denoised volume (GPU)'); +%% +fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n'); +iter_diff = 300; % number of diffusion iterations +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(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; +rmse_diff4 = (RMSE(Ideal3D(:),u_diff4(:))); +fprintf('%s %f \n', 'RMSE error for Anis.Diff of 4th order is:', rmse_diff4); +figure; imshow(u_diff4(:,:,7), [0 1]); title('Diffusion 4thO denoised volume (CPU)'); +%% +% fprintf('Denoise using Fourth-order anisotropic diffusion model (GPU) \n'); +% iter_diff = 300; % number of diffusion iterations +% lambda_regDiff = 3.5; % regularisation for the diffusivity +% sigmaPar = 0.02; % edge-preserving parameter +% tau_param = 0.0015; % time-marching constant +% tic; u_diff4_g = Diffusion_4thO_GPU(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param); toc; +% rmse_diff4 = (RMSE(Ideal3D(:),u_diff4_g(:))); +% 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'); + +% create another volume (reference) with slightly less amount of noise +vol3D_ref = zeros(N,N,slices, 'single'); +for i = 1:slices +vol3D_ref(:,:,i) = Im + .01*randn(size(Im)); +end +vol3D_ref(vol3D_ref < 0) = 0; +% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) + +iter_fgp = 300; % number of FGP iterations +epsil_tol = 1.0e-05; % tolerance +eta = 0.2; % Reference image gradient smoothing constant +tic; u_fgp_dtv = FGP_dTV(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; +figure; imshow(u_fgp_dtv(:,:,7), [0 1]); title('FGP-dTV denoised volume (CPU)'); +%% +fprintf('Denoise a volume using the FGP-dTV model (GPU) \n'); + +% create another volume (reference) with slightly less amount of noise +vol3D_ref = zeros(N,N,slices, 'single'); +for i = 1:slices +vol3D_ref(:,:,i) = Im + .01*randn(size(Im)); +end +vol3D_ref(vol3D_ref < 0) = 0; +% vol3D_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV) + +iter_fgp = 300; % number of FGP iterations +epsil_tol = 1.0e-05; % tolerance +eta = 0.2; % Reference image gradient smoothing constant +tic; u_fgp_dtv_g = FGP_dTV_GPU(single(vol3D), single(vol3D_ref), lambda_reg, iter_fgp, epsil_tol, eta); toc; +figure; imshow(u_fgp_dtv_g(:,:,7), [0 1]); title('FGP-dTV denoised volume (GPU)'); +%% |