comparison examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m @ 6:f72603404233

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+%% DICTIONARY LEARNING FOR IMAGE DENOISING
+%   This file contains an example of how SMALLbox can be used to test different
+%   dictionary learning techniques in Image Denoising problem.
+%   It calls generateImageDenoiseProblem that will let you to choose image,
+%   add noise and use noisy image to generate training set for dictionary
+%   learning.
+%   Three dictionary learning techniques were compared:
+%   -   KSVD - M. Elad, R. Rubinstein, and M. Zibulevsky, "Efficient
+%              Implementation of the K-SVD Algorithm using Batch Orthogonal
+%              Matching Pursuit", Technical Report - CS, Technion, April 2008.
+%   -   KSVDS - R. Rubinstein, M. Zibulevsky, and M. Elad, "Learning Sparse
+%               Dictionaries for Sparse Signal Approximation", Technical
+%               Report - CS, Technion, June 2009.
+%   -   SPAMS - J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online
+%               Dictionary Learning for Sparse Coding. International
+%               Conference on Machine Learning,Montreal, Canada, 2009
+%
+%
+% Ivan Damnjanovic 2010
+%%
+
+clear;
+
+%   If you want to load the image outside of generateImageDenoiseProblem
+%   function uncomment following lines. This can be useful if you want to
+%   denoise more then one image for example.
+
+% TMPpath=pwd;
+% FS=filesep;
+% [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+% cd([pathstr1,FS,'data',FS,'images']);
+% [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
+% [pathstr, name, ext, versn] = fileparts(filename);
+% test_image = imread(filename);
+% test_image = double(test_image);
+% cd(TMPpath);
+% SMALL.Problem.name=name;
+
+
+% Defining Image Denoising Problem as Dictionary Learning
+% Problem. As an input we set the number of training patches.
+
+SMALL.Problem = generateImageDenoiseProblem('', 40000);
+
+
+%%
+%   Use KSVD Dictionary Learning Algorithm to Learn overcomplete dictionary
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values
+
+SMALL.DL(1)=SMALL_init_DL();
+
+% Defining the parameters needed for dictionary learning
+
+SMALL.DL(1).toolbox = 'KSVD';
+SMALL.DL(1).name = 'ksvd';
+
+%   Defining the parameters for KSVD
+%   In this example we are learning 256 atoms in 20 iterations, so that
+%   every patch in the training set can be represented with target error in
+%   L2-norm (EData)
+%   Type help ksvd in MATLAB prompt for more options.
+
+Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
+SMALL.DL(1).param=struct(...
+    'Edata', Edata,...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'iternum', 20,...
+    'memusage', 'high');
+
+%   Learn the dictionary
+
+SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));
+
+%   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(1).D;
+
+
+%%
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.solver(1)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(1).toolbox='ompbox';
+SMALL.solver(1).name='ompdenoise';
+
+%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+%   but backward compatible with KSVD definition of denoising
+
+SMALL.solver(1)=SMALL_denoise(SMALL.Problem, SMALL.solver(1));
+
+%%
+% Use KSVDS Dictionary Learning Algorithm to denoise image
+
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.DL(2)=SMALL_init_DL();
+
+% Defining the parameters needed for dictionary learning
+
+SMALL.DL(2).toolbox = 'KSVDS';
+SMALL.DL(2).name = 'ksvds';
+
+%   Defining the parameters for KSVDS
+%   In this example we are learning 256 atoms in 20 iterations, so that
+%   every patch in the training set can be represented with target error in
+%   L2-norm (EDataS). We also impose "double sparsity" - dictionary itself
+%   has to be sparse in the given base dictionary (Tdict - number of
+%   nonzero elements per atom).
+%   Type help ksvds in MATLAB prompt for more options.
+
+EdataS=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
+SMALL.DL(2).param=struct(...
+    'Edata', EdataS, ...
+    'Tdict', 6,...
+    'stepsize', 1,...
+    'dictsize', SMALL.Problem.p,...
+    'iternum', 20,...
+    'memusage', 'high');
+SMALL.DL(2).param.initA = speye(SMALL.Problem.p);
+SMALL.DL(2).param.basedict{1} = odctdict(8,16);
+SMALL.DL(2).param.basedict{2} = odctdict(8,16);
+
+% Learn the dictionary
+
+SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
+
+%   Set SMALL.Problem.A dictionary and SMALL.Problem.basedictionary
+%   (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(2).D;
+SMALL.Problem.basedict{1} = SMALL.DL(2).param.basedict{1};
+SMALL.Problem.basedict{2} = SMALL.DL(2).param.basedict{2};
+
+%%
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.solver(2)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(2).toolbox='ompsbox';
+SMALL.solver(2).name='ompsdenoise';
+
+%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+%   but backward compatible with KSVD definition of denoising
+%   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));
+
+%%
+% Plot results and save midi files
+
+% show results %
+
+SMALL_ImgDeNoiseResult(SMALL);