From b9fafd363d1d181a4a8b42ea4038924097207913 Mon Sep 17 00:00:00 2001
From: Daniil Kazantsev <dkazanc@hotmail.com>
Date: Mon, 9 Apr 2018 13:41:06 +0100
Subject: major renaming and new  3D demos for Matlab

---
 Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m | 44 ++++++++++++++++++++++++++++
 Wrappers/Matlab/demos/demoMatlab_denoise.m   |  8 ++---
 2 files changed, 48 insertions(+), 4 deletions(-)
 create mode 100644 Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m

(limited to 'Wrappers/Matlab/demos')

diff --git a/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
new file mode 100644
index 0000000..f5c3ad1
--- /dev/null
+++ b/Wrappers/Matlab/demos/demoMatlab_3Ddenoise.m
@@ -0,0 +1,44 @@
+% Volume (3D) denoising demo using CCPi-RGL
+
+addpath('../mex_compile/installed');
+addpath('../../../data/');
+
+N = 256; 
+slices = 30;
+vol3D = zeros(N,N,slices, 'single');
+Im = double(imread('lena_gray_256.tif'))/255;  % loading image
+for i = 1:slices
+vol3D(:,:,i) = Im + .05*randn(size(Im)); 
+end
+vol3D(vol3D < 0) = 0;
+figure; imshow(vol3D(:,:,15), [0 1]); title('Noisy image');
+
+%%
+fprintf('Denoise using ROF-TV model (CPU) \n');
+lambda_rof = 0.03; % regularisation parameter
+tau_rof = 0.0025; % time-marching constant 
+iter_rof = 1000; % number of ROF iterations
+tic; u_rof = ROF_TV(single(vol3D), lambda_rof, iter_rof, tau_rof); toc; 
+figure; imshow(u_rof(:,:,15), [0 1]); title('ROF-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise using ROF-TV model (GPU) \n');
+% lambda_rof = 0.03; % regularisation parameter
+% tau_rof = 0.0025; % time-marching constant 
+% iter_rof = 1000; % number of ROF iterations
+% tic; u_rofG = ROF_TV_GPU(single(vol3D), lambda_rof, iter_rof, tau_rof); toc;
+% figure; imshow(u_rofG(:,:,15), [0 1]); title('ROF-TV denoised volume (GPU)');
+%%
+fprintf('Denoise using FGP-TV model (CPU) \n');
+lambda_fgp = 0.03; % regularisation parameter
+iter_fgp = 500; % number of FGP iterations
+epsil_tol =  1.0e-05; % tolerance
+tic; u_fgp = FGP_TV(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; 
+figure; imshow(u_fgp(:,:,15), [0 1]); title('FGP-TV denoised volume (CPU)');
+%%
+% fprintf('Denoise using FGP-TV model (GPU) \n');
+% lambda_fgp = 0.03; % regularisation parameter
+% iter_fgp = 500; % number of FGP iterations
+% epsil_tol =  1.0e-05; % tolerance
+% tic; u_fgpG = FGP_TV_GPU(single(vol3D), lambda_fgp, iter_fgp, epsil_tol); toc; 
+% figure; imshow(u_fgpG(:,:,15), [0 1]); title('FGP-TV denoised volume (GPU)');
+%%
\ No newline at end of file
diff --git a/Wrappers/Matlab/demos/demoMatlab_denoise.m b/Wrappers/Matlab/demos/demoMatlab_denoise.m
index 7258e5e..ab4e95d 100644
--- a/Wrappers/Matlab/demos/demoMatlab_denoise.m
+++ b/Wrappers/Matlab/demos/demoMatlab_denoise.m
@@ -9,28 +9,28 @@ figure; imshow(u0, [0 1]); title('Noisy image');
 
 %%
 fprintf('Denoise using ROF-TV model (CPU) \n');
-lambda_rof = 0.03; % regularization parameter
+lambda_rof = 0.03; % regularisation parameter
 tau_rof = 0.0025; % time-marching constant 
 iter_rof = 2000; % number of ROF iterations
 tic; u_rof = ROF_TV(single(u0), lambda_rof, iter_rof, tau_rof); toc; 
 figure; imshow(u_rof, [0 1]); title('ROF-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using ROF-TV model (GPU) \n');
-% lambda_rof = 0.03; % regularization parameter
+% lambda_rof = 0.03; % regularisation parameter
 % tau_rof = 0.0025; % time-marching constant 
 % iter_rof = 2000; % number of ROF iterations
 % tic; u_rofG = ROF_TV_GPU(single(u0), lambda_rof, iter_rof, tau_rof); toc;
 % figure; imshow(u_rofG, [0 1]); title('ROF-TV denoised image (GPU)');
 %%
 fprintf('Denoise using FGP-TV model (CPU) \n');
-lambda_fgp = 0.03; % regularization parameter
+lambda_fgp = 0.03; % regularisation parameter
 iter_fgp = 1000; % number of FGP iterations
 epsil_tol =  1.0e-05; % tolerance
 tic; u_fgp = FGP_TV(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; 
 figure; imshow(u_fgp, [0 1]); title('FGP-TV denoised image (CPU)');
 %%
 % fprintf('Denoise using FGP-TV model (GPU) \n');
-% lambda_fgp = 0.03; % regularization parameter
+% lambda_fgp = 0.03; % regularisation parameter
 % iter_fgp = 1000; % number of FGP iterations
 % epsil_tol =  1.0e-05; % tolerance
 % tic; u_fgpG = FGP_TV_GPU(single(u0), lambda_fgp, iter_fgp, epsil_tol); toc; 
-- 
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