UPDATE: docs -> demos and update paths in m and py demos

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)');
+%%