From b901525766f8e3473ef58a19bf3fadc178d3778c Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Mon, 11 Mar 2019 22:27:57 +0000
Subject: fix for matlab wrappers
---
demos/demoMatlab_denoise.m | 36 +++++++++++++++++++-----------------
1 file changed, 19 insertions(+), 17 deletions(-)
(limited to 'demos/demoMatlab_denoise.m')
diff --git a/demos/demoMatlab_denoise.m b/demos/demoMatlab_denoise.m
index a22b40a..7581068 100644
--- a/demos/demoMatlab_denoise.m
+++ b/demos/demoMatlab_denoise.m
@@ -19,7 +19,7 @@ iter_rof = 2000; % number of ROF iterations
tau_rof = 0.001; % time-marching constant
epsil_tol = 0.0; % tolerance
tic; [u_rof,infovec] = ROF_TV(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc;
-%energyfunc_val_rof = TV_energy(single(u_rof),single(u0),lambda_reg, 1); % get energy function value
+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);
[ssimval] = ssim(u_rof*255,single(Im)*255);
@@ -27,7 +27,7 @@ fprintf('%s %f \n', 'MSSIM error for ROF-TV is:', ssimval);
figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
%%
% fprintf('Denoise using the ROF-TV model (GPU) \n');
-% tic; u_rofG = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc;
+% tic; [u_rofG,infovec] = ROF_TV_GPU(single(u0), lambda_reg, iter_rof, tau_rof, epsil_tol); toc;
% figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
%%
fprintf('Denoise using the FGP-TV model (CPU) \n');
@@ -59,8 +59,6 @@ fprintf('%s %f \n', 'MSSIM error for SB-TV is:', ssimval);
figure; imshow(u_sb, [0 1]); title('SB-TV denoised image (CPU)');
%%
% fprintf('Denoise using the SB-TV model (GPU) \n');
-% iter_sb = 80; % number of SB iterations
-% epsil_tol = 1.0e-06; % tolerance
% 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)');
%%
@@ -69,7 +67,8 @@ iter_diff = 450; % number of diffusion iterations
lambda_regDiff = 0.025; % regularisation for the diffusivity
sigmaPar = 0.015; % edge-preserving parameter
tau_param = 0.02; % time-marching constant
-tic; u_diff = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber'); toc;
+epsil_tol = 1.0e-06; % tolerance
+tic; [u_diff,infovec] = NonlDiff(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, 'Huber', epsil_tol); toc;
rmseDiffus = (RMSE(u_diff(:),Im(:)));
fprintf('%s %f \n', 'RMSE error for Nonlinear Diffusion is:', rmseDiffus);
[ssimval] = ssim(u_diff*255,single(Im)*255);
@@ -85,11 +84,11 @@ figure; imshow(u_diff, [0 1]); title('Diffusion denoised image (CPU)');
% figure; imshow(u_diff_g, [0 1]); title('Diffusion denoised image (GPU)');
%%
fprintf('Denoise using the TGV model (CPU) \n');
-lambda_TGV = 0.034; % regularisation parameter
+lambda_TGV = 0.045; % regularisation parameter
alpha1 = 1.0; % parameter to control the first-order term
-alpha0 = 1.0; % 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;
+alpha0 = 2.0; % parameter to control the second-order term
+iter_TGV = 2500; % number of Primal-Dual iterations for TGV
+tic; [u_tgv,infovec] = 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);
[ssimval] = ssim(u_tgv*255,single(Im)*255);
@@ -109,11 +108,12 @@ figure; imshow(u_tgv, [0 1]); title('TGV denoised image (CPU)');
% figure; imshow(u_tgv_gpu, [0 1]); title('TGV denoised image (GPU)');
%%
fprintf('Denoise using the ROF-LLT model (CPU) \n');
-lambda_ROF = 0.016; % ROF regularisation parameter
-lambda_LLT = lambda_reg*0.25; % LLT regularisation parameter
-iter_LLT = 500; % iterations
+lambda_ROF = 0.02; % ROF regularisation parameter
+lambda_LLT = 0.01; % LLT regularisation parameter
+iter_LLT = 1000; % iterations
tau_rof_llt = 0.0025; % time-marching constant
-tic; u_rof_llt = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt); toc;
+epsil_tol = 1.0e-06; % tolerance
+tic; [u_rof_llt,infovec] = LLT_ROF(single(u0), lambda_ROF, lambda_LLT, iter_LLT, tau_rof_llt,epsil_tol); toc;
rmseROFLLT = (RMSE(u_rof_llt(:),Im(:)));
fprintf('%s %f \n', 'RMSE error for TGV is:', rmseROFLLT);
[ssimval] = ssim(u_rof_llt*255,single(Im)*255);
@@ -132,10 +132,11 @@ figure; imshow(u_rof_llt, [0 1]); title('ROF-LLT denoised image (CPU)');
%%
fprintf('Denoise using Fourth-order anisotropic diffusion model (CPU) \n');
iter_diff = 800; % number of diffusion iterations
-lambda_regDiff = 3.5; % regularisation for the diffusivity
-sigmaPar = 0.021; % edge-preserving parameter
+lambda_regDiff = 2.5; % regularisation for the diffusivity
+sigmaPar = 0.03; % 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;
+epsil_tol = 1.0e-06; % tolerance
+tic; [u_diff4,infovec] = Diffusion_4thO(single(u0), lambda_regDiff, sigmaPar, iter_diff, tau_param, epsil_tol); toc;
rmseDiffHO = (RMSE(u_diff4(:),Im(:)));
fprintf('%s %f \n', 'RMSE error for Fourth-order anisotropic diffusion is:', rmseDiffHO);
[ssimval] = ssim(u_diff4*255,single(Im)*255);
@@ -174,10 +175,11 @@ fprintf('Denoise using the FGP-dTV model (CPU) \n');
u_ref = Im + .01*randn(size(Im)); u_ref(u_ref < 0) = 0;
% u_ref = zeros(size(Im),'single'); % pass zero reference (dTV -> TV)
+lambda_reg = 0.04;
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;
+tic; [u_fgp_dtv,infovec] = 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)');
--
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