Mercurial > hg > smallbox
view examples/ALPS solvers tests/SMALL_ImgDenoise_DL_test_KSVDvsTwoStepALPSandMahile.m @ 204:5fe60504a6a9 luisf_dev
Merge from 203:f3b6ddd2f04f
author | luisf <luis.figueira@eecs.qmul.ac.uk> |
---|---|
date | Tue, 20 Mar 2012 15:53:15 +0000 |
parents | 9c418bea7f6a |
children |
line wrap: on
line source
%% 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. % - RLS-DLA - Skretting, K.; Engan, K.; , "Recursive Least Squares % Dictionary Learning Algorithm," Signal Processing, IEEE Transactions on, % vol.58, no.4, pp.2121-2130, April 2010 % % 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=4:4 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 overcomplete DCT % here % Set SMALL.Problem.A dictionary to be oDCT (i.e. Problem.initdict - % since initial dictionaruy is already set to be oDCT when generating the % denoising problem % Initialising solver structure % Setting solver structure fields (toolbox, name, param, solution, % reconstructed and time) to zero values SMALL.solver(2)=SMALL_init_solver('ALPS','AgebraicPursuit','',1); % Defining the parameters needed for image denoising SMALL.solver(2).param=struct(... 'tolerance',1e-05,... 'sparsity', 32,... 'mode', 0,... 'memory', 1,... 'iternum', 50); % Initialising Dictionary structure % Setting Dictionary structure fields (toolbox, name, param, D and time) % to zero values SMALL.DL(2)=SMALL_init_DL('TwoStepDL', 'Mailhe', '', 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', 40,... 'show_dict', 1); % 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)); %% % In the b1 field all patches from the image are stored. For RLS-DLA we % will first exclude all the patches that have l2 norm smaller then % threshold and then take min(40000, number_of_remaining_patches) in % ascending order as our training set (SMALL.Problem.b) X=SMALL.Problem.b1; X_norm=sqrt(sum(X.^2, 1)); [X_norm_sort, p]=sort(X_norm); p1=p(X_norm_sort>Edata); if size(p1,2)>40000 p2 = randperm(size(p1,2)); p2=sort(p2(1:40000)); size(p2,2) SMALL.Problem.b=X(:,p1(p2)); else size(p1,2) SMALL.Problem.b=X(:,p1); end % Forgetting factor for RLS-DLA algorithm, in this case we are using % fixed value lambda=0.9998 % Use Recursive Least Squares % to Learn overcomplete dictionary % 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 = 'SMALL'; SMALL.DL(3).name = 'SMALL_rlsdla'; SMALL.DL(3).param=struct(... 'Edata', Edata,... 'initdict', SMALL.Problem.initdict,... 'dictsize', SMALL.Problem.p,... 'forgettingMode', 'FIX',... 'forgettingFactor', lambda,... 'show_dict', 1000); SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3)); % Initialising solver structure % Setting solver structure fields (toolbox, name, param, solution, % reconstructed and time) to zero values SMALL.Problem.A = SMALL.DL(3).D; SMALL.Problem.reconstruct = @(x) ImageDenoise_reconstruct(x, SMALL.Problem); 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); SMALL.solver(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3)); SMALL.solver(3).reconstructed.psnr % 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).psnr.rlsdla=SMALL.solver(3).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).vmrse.rlsdla=SMALL.solver(3).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).ssim.rlsdla=SMALL.solver(3).reconstructed.ssim; results(noise_ind,im_num).time.ksvd=SMALL.solver(1).time+SMALL.DL(1).time; results(noise_ind,im_num).time.rlsdla.time=SMALL.solver(3).time+SMALL.DL(3).time; clear SMALL; end end % save results.mat results