Mercurial > hg > smallbox
view examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m @ 162:88578ec2f94a danieleb
Updated grassmannian function and minor debugs
author | Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk> |
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date | Wed, 31 Aug 2011 13:52:23 +0100 |
parents | 8e660fd14774 |
children | f42aa8bcb82f |
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%% 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. % 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 % % % 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, 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; 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) ImgDenoise_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) ImgDenoise_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)); %% % 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; % Setting up reconstruction function SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem); % 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 image denoising SMALL.solver(3).toolbox='ompbox'; SMALL.solver(3).name='omp2'; SMALL.solver(3).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(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3)); %% % Plot results and save midi files % show results % SMALL_ImgDeNoiseResult(SMALL);