Mercurial > hg > smallbox
view examples/MajorizationMinimization tests/SMALL_ImgDenoise_DL_test_KSVDvsMajorizationMinimization.m @ 207:233e75809e4a luisf_dev
Accelerated the code for LGD and optimal grandient descent
author | bmailhe |
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date | Wed, 21 Mar 2012 14:12:25 +0000 |
parents | 9c418bea7f6a |
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%% Dictionary Learning for Image Denoising - KSVD vs Recursive Least Squares % % 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. % % - MMDL - M. Yaghoobi, T. Blumensath and M. Davies, "Dictionary Learning % for Sparse Approximations with the Majorization Method", IEEE % Trans. on Signal Processing, Vol. 57, No. 6, pp 2178-2191, 2009. % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2011 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. % %% % 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. % Here we are loading test_image.mat that contains structure with 5 images : lena, % barbara,boat, house and peppers. clear; TMPpath=pwd; FS=filesep; [pathstr1, name, ext] = fileparts(which('SMALLboxSetup.m')); cd([pathstr1,FS,'data',FS,'images']); load('test_image.mat'); cd(TMPpath); % Deffining the noise levels that we want to test noise_level=[10 20 25 50 100]; % Here we loop through different noise levels and images for noise_ind=2:2 for im_num=1:1 % Defining Image Denoising Problem as Dictionary Learning % Problem. As an input we set the number of training patches. SMALL.Problem = generateImageDenoiseProblem(test_image(im_num).i, 40000, '',256, noise_level(noise_ind)); SMALL.Problem.name=int2str(im_num); Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain; maxatoms = floor(prod(SMALL.Problem.blocksize)/2); % results structure is to store all results results(noise_ind,im_num).noisy_psnr=SMALL.Problem.noisy_psnr; %% % 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. SMALL.DL(1).param=struct(... 'Edata', Edata,... 'initdict', SMALL.Problem.initdict,... 'dictsize', SMALL.Problem.p,... 'exact', 1, ... '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 %% % For comparison purposes we will denoise image with Majorization % Minimization method % % 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='ompbox'; SMALL.solver(2).name='omp2'; SMALL.solver(2).param=struct(... 'epsilon',Edata,... 'maxatoms', maxatoms); % Initialising Dictionary structure % Setting Dictionary structure fields (toolbox, name, param, D and time) % to zero values SMALL.DL(2)=SMALL_init_DL('MMbox', 'MM_cn', '', 1); % Defining the parameters for MOD % 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. SMALL.DL(2).param=struct(... 'solver', SMALL.solver(2),... 'initdict', SMALL.Problem.initdict,... 'dictsize', SMALL.Problem.p,... 'iternum', 20,... 'iterDictUpdate', 1000,... 'epsDictUpdate', 1e-7,... 'cvset',0,... 'show_dict', 0); % Learn the dictionary SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2)); % 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(2).D; SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem); % 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)); % show results % SMALL_ImgDeNoiseResult(SMALL); results(noise_ind,im_num).psnr.ksvd=SMALL.solver(1).reconstructed.psnr; results(noise_ind,im_num).psnr.odct=SMALL.solver(2).reconstructed.psnr; results(noise_ind,im_num).vmrse.ksvd=SMALL.solver(1).reconstructed.vmrse; results(noise_ind,im_num).vmrse.odct=SMALL.solver(2).reconstructed.vmrse; results(noise_ind,im_num).ssim.ksvd=SMALL.solver(1).reconstructed.ssim; results(noise_ind,im_num).ssim.odct=SMALL.solver(2).reconstructed.ssim; results(noise_ind,im_num).time.ksvd=SMALL.solver(1).time+SMALL.DL(1).time; %clear SMALL; end end % save results.mat results