diff examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m @ 78:f69ae88b8be5

added Rice Wavelet Toolbox with my modification, so it can be compiled on newer systems.

--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Wed Mar 23 17:08:55 2011 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Fri Mar 25 15:27:33 2011 +0000
@@ -30,6 +30,7 @@
 [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
 cd([pathstr1,FS,'data',FS,'images']);
 load('test_image.mat');
+cd(TMPpath);
 % [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
 % [pathstr, name, ext, versn] = fileparts(filename);
 % test_image = imread(filename);
@@ -41,9 +42,9 @@
 % Defining Image Denoising Problem as Dictionary Learning
 % Problem. As an input we set the number of training patches.
 for noise_ind=1:1
-for im_num=4:4
+for im_num=2:2
 SMALL.Problem = generateImageDenoiseProblem(test_image(im_num).i, 40000, '',256, noise_level(noise_ind));
-SMALL.Problem.name=im_num;
+SMALL.Problem.name=int2str(im_num);
 
 results(noise_ind,im_num).noisy_psnr=SMALL.Problem.noisy_psnr;