fixes all matlab issues

1 files changed, 0 insertions, 198 deletions

diff --git a/demos/demoMatlab_3Ddenoise.m b/demos/demoMatlab_3Ddenoise.m
deleted file mode 100644
index 3942eea..0000000
--- a/demos/demoMatlab_3Ddenoise.m
+++ /dev/null
@@ -1,198 +0,0 @@
-% 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 = 15;
-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(:,:,7), [0 1]); title('Noisy image');
-
-%%
-fprintf('Denoise a volume using the ROF-TV model (CPU) \n');
-lambda_reg = 0.03; % regularsation parameter for all methods
-tau_rof = 0.0025; % time-marching constant 
-iter_rof = 300; % number of ROF iterations
-epsil_tol =  0.0; % tolerance
-tic; [u_rof,infovec] = ROF_TV(single(vol3D), lambda_reg, iter_rof, tau_rof, epsil_tol); 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');
-% lambda_reg = 0.03; % regularsation parameter for all methods
-% tau_rof = 0.0025; % time-marching constant 
-% iter_rof = 300; % number of ROF iterations
-% epsil_tol =  0.0; % tolerance
-% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_reg, iter_rof, tau_rof, epsil_tol); 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');
-lambda_reg = 0.03; % regularsation parameter for all methods
-iter_fgp = 300; % number of FGP iterations
-epsil_tol =  0.0; % tolerance
-tic; [u_fgp,infovec] = 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');
-% lambda_reg = 0.03; % regularsation parameter for all methods
-% iter_fgp = 300; % number of FGP iterations
-% epsil_tol =  0.0; % 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 =  0.0; % tolerance
-tic; [u_sb,infovec] = 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 =  0.0; % 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 
-epsil_tol =  0.0; % tolerance
-tic; [u_rof_llt, infovec] = LLT_ROF(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); 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 
-% epsil_tol =  0.0; % tolerance
-% tic; u_rof_llt_g = LLT_ROF_GPU(single(vol3D), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt, epsil_tol); 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 
-epsil_tol =  0.0; % tolerance
-tic; [u_diff, infovec]  = NonlDiff(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); 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', epsil_tol); 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 
-epsil_tol =  0.0; % tolerance
-tic; u_diff4 = Diffusion_4thO(single(vol3D), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); 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, epsil_tol); 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
-L2 =  12.0; % convergence parameter
-iter_TGV = 500; % number of Primal-Dual iterations for TGV
-epsil_tol =  0.0; % tolerance
-tic; u_tgv = TGV(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); 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)');
-%%
-% fprintf('Denoise using the TGV model (GPU) \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_gpu = TGV_GPU(single(vol3D), lambda_TGV, alpha1, alpha0, iter_TGV, L2, epsil_tol); toc; 
-% rmseTGV = RMSE(Ideal3D(:),u_tgv_gpu(:));
-% fprintf('%s %f \n', 'RMSE error for TGV is:', rmseTGV);
-% figure; imshow(u_tgv_gpu(:,:,3), [0 1]); title('TGV denoised volume (GPU)');
-%%
-%>>>>>>>>>>>>>> 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 =  0.0; % 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 =  0.0; % 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)');
-%%