comparison examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSKSVD.m @ 236:5f4e47b78f2b ver_2.0_alpha1

Added example.

+%%  Dictionary Learning for Image Denoising - KSVD vs KSVDS vs SPAMS
+%
+%   *WARNING!* You should have SPAMS in your search path in order for this
+%   script to work.Due to licensing issues SPAMS can not be automatically 
+%   provided in SMALLbox (http://www.di.ens.fr/willow/SPAMS/downloads.html).
+%
+%   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.
+%   Two 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.
+%
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 Ivan Damnjanovic.
+%
+%   This program is free software; you can redistribute it and/or
+%   modify it under the terms of the GNU General Public License as
+%   published by the Free Software Foundation; either version 2 of the
+%   License, or (at your option) any later version.  See the file
+%   COPYING included with this distribution for more information.
+%
+%%
+
+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] = fileparts(which('SMALLboxSetup.m'));
+% cd([pathstr1,FS,'data',FS,'images']);
+% [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
+% [pathstr, name, ext] = 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;
+maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
+
+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;
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem);
+
+%%
+%   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='omp2';
+SMALL.solver(1).param=struct(...
+    'epsilon',Edata,...
+    'maxatoms', maxatoms); 
+
+%   Denoising the image - find the sparse solution in the learned
+%   dictionary for all patches in the image and the end it uses
+%   reconstruction function to reconstruct the patches and put them into a
+%   denoised image
+
+SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
+
+%   Show PSNR after reconstruction
+
+SMALL.solver(1).reconstructed.psnr
+
+%%
+% 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};
+
+%   Setting up reconstruction function
+
+SparseDict=1;
+SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem, SparseDict);
+
+%   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='omps2';
+SMALL.solver(2).param=struct(...
+    'epsilon',Edata,...
+    'maxatoms', maxatoms); 
+
+%   Denoising the image - find the sparse solution in the learned
+%   dictionary for all patches in the image and the end it uses
+%   reconstruction function to reconstruct the patches and put them into a
+%   denoised image
+
+SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+
+
+%%
+% Plot results and save midi files
+
+% show results %
+
+SMALL_ImgDeNoiseResult(SMALL);