# HG changeset patch # User luisf # Date 1334854748 -3600 # Node ID 5f4e47b78f2b857208951b7570d2d3c5041603ee # Parent 1f5c793c2b18692ad984e1f3fcc3e1e82e5031c8 Added example. diff -r 1f5c793c2b18 -r 5f4e47b78f2b examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSKSVD.m --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSKSVD.m Thu Apr 19 17:59:08 2012 +0100 @@ -0,0 +1,204 @@ +%% 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);