comparison examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepKSVD.m @ 161:f42aa8bcb82f ivand_dev

debug and clean the SMALLbox Problems code
author Ivan Damnjanovic lnx <ivan.damnjanovic@eecs.qmul.ac.uk>
date Wed, 31 Aug 2011 12:02:19 +0100
parents af307f247ac7
children 9c418bea7f6a
comparison
equal deleted inserted replaced
155:b14209313ba4 161:f42aa8bcb82f
101 % (backward compatiblity with SPARCO: solver structure communicate 101 % (backward compatiblity with SPARCO: solver structure communicate
102 % only with Problem structure, ie no direct communication between DL and 102 % only with Problem structure, ie no direct communication between DL and
103 % solver structures) 103 % solver structures)
104 104
105 SMALL.Problem.A = SMALL.DL(1).D; 105 SMALL.Problem.A = SMALL.DL(1).D;
106 SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem); 106 SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
107 107
108 %% 108 %%
109 % Initialising solver structure 109 % Initialising solver structure
110 % Setting solver structure fields (toolbox, name, param, solution, 110 % Setting solver structure fields (toolbox, name, param, solution,
111 % reconstructed and time) to zero values 111 % reconstructed and time) to zero values
180 % (backward compatiblity with SPARCO: solver structure communicate 180 % (backward compatiblity with SPARCO: solver structure communicate
181 % only with Problem structure, ie no direct communication between DL and 181 % only with Problem structure, ie no direct communication between DL and
182 % solver structures) 182 % solver structures)
183 183
184 SMALL.Problem.A = SMALL.DL(2).D; 184 SMALL.Problem.A = SMALL.DL(2).D;
185 SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem); 185 SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
186 186
187 % Denoising the image - find the sparse solution in the learned 187 % Denoising the image - find the sparse solution in the learned
188 % dictionary for all patches in the image and the end it uses 188 % dictionary for all patches in the image and the end it uses
189 % reconstruction function to reconstruct the patches and put them into a 189 % reconstruction function to reconstruct the patches and put them into a
190 % denoised image 190 % denoised image