comparison examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m @ 13:cd55209c69e1

(none)

 %   Pay attention that since implicit base dictionary is used, denoising
 %   can be much faster then using explicit dictionary in KSVD example.
 
 SMALL.solver(2)=SMALL_denoise(SMALL.Problem, SMALL.solver(2));
 
-%%
-%   Use SPAMS Online Dictionary Learning Algorithm
-%   to Learn overcomplete dictionary (Julien Mairal 2009)
-%   (If you have not installed SPAMS please comment the following two cells)
-
-%   Initialising Dictionary structure
-%   Setting Dictionary structure fields (toolbox, name, param, D and time)
-%   to zero values
-
-SMALL.DL(3)=SMALL_init_DL();
-
-%   Defining fields needed for dictionary learning
-
-SMALL.DL(3).toolbox = 'SPAMS';
-SMALL.DL(3).name = 'mexTrainDL';
-
-%   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
-
-SMALL.DL(3).param=struct(...
-    'D', SMALL.Problem.initdict,...
-    'K', SMALL.Problem.p,...
-    'lambda', 2,...
-    'iter', 200,...
-    'mode', 3, ...
-    'modeD', 0);
-
-%   Learn the dictionary
-
-SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3));
-
-%   Set SMALL.Problem.A dictionary
-%   (backward compatiblity with SPARCO: solver structure communicate
-%   only with Problem structure, ie no direct communication between DL and
-%   solver structures)
-
-SMALL.Problem.A = SMALL.DL(3).D;
-
-
-%%
-%   Initialising solver structure
-%   Setting solver structure fields (toolbox, name, param, solution,
-%   reconstructed and time) to zero values
-
-SMALL.solver(3)=SMALL_init_solver;
-
-% Defining the parameters needed for denoising
-
-SMALL.solver(3).toolbox='ompbox';
-SMALL.solver(3).name='ompdenoise';
-
-%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
-%   but backward compatible with KSVD definition of denoising
-
-SMALL.solver(3)=SMALL_denoise(SMALL.Problem, SMALL.solver(3));
+% %%
+% %   Use SPAMS Online Dictionary Learning Algorithm
+% %   to Learn overcomplete dictionary (Julien Mairal 2009)
+% %   (If you have not installed SPAMS please comment the following two cells)
+% 
+% %   Initialising Dictionary structure
+% %   Setting Dictionary structure fields (toolbox, name, param, D and time)
+% %   to zero values
+% 
+% SMALL.DL(3)=SMALL_init_DL();
+% 
+% %   Defining fields needed for dictionary learning
+% 
+% SMALL.DL(3).toolbox = 'SPAMS';
+% SMALL.DL(3).name = 'mexTrainDL';
+% 
+% %   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
+% 
+% SMALL.DL(3).param=struct(...
+%     'D', SMALL.Problem.initdict,...
+%     'K', SMALL.Problem.p,...
+%     'lambda', 2,...
+%     'iter', 200,...
+%     'mode', 3, ...
+%     'modeD', 0);
+% 
+% %   Learn the dictionary
+% 
+% SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3));
+% 
+% %   Set SMALL.Problem.A dictionary
+% %   (backward compatiblity with SPARCO: solver structure communicate
+% %   only with Problem structure, ie no direct communication between DL and
+% %   solver structures)
+% 
+% SMALL.Problem.A = SMALL.DL(3).D;
+% 
+% 
+% %%
+% %   Initialising solver structure
+% %   Setting solver structure fields (toolbox, name, param, solution,
+% %   reconstructed and time) to zero values
+% 
+% SMALL.solver(3)=SMALL_init_solver;
+% 
+% % Defining the parameters needed for denoising
+% 
+% SMALL.solver(3).toolbox='ompbox';
+% SMALL.solver(3).name='ompdenoise';
+% 
+% %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+% %   but backward compatible with KSVD definition of denoising
+% 
+% SMALL.solver(3)=SMALL_denoise(SMALL.Problem, SMALL.solver(3));
 
 %%
 % Plot results and save midi files
 
 % show results %