Mercurial > hg > smallbox
view examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m @ 210:f12a476a4977 luisf_dev
Added help comments to SMALL_two_step_DL.m
author | bmailhe |
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date | Wed, 21 Mar 2012 17:25:40 +0000 |
parents | 9c418bea7f6a |
children | 8b3c71bb44eb |
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%% Dictionary Learning for Image Denoising - SPAMS parameter test % % *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. % This example can be used to test SPAMS for different values of % parameter lambda. In no way it represents extensive testing of image % denoising. It should only give an idea how SMALL structure can be used % for testing. % % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2010 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 all; %% Load an image 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); %% % number of different values we want to test n =4; lambda=zeros(1,n); time = zeros(2,n); psnr = zeros(2,n); for i=1:n % Here we want to test time spent and quality of denoising for % different lambda parameters. lambda(i)=1+i*0.5; % Defining Image Denoising Problem as Dictionary Learning Problem. SMALL.Problem = generateImageDenoiseProblem(test_image); SMALL.Problem.name=name; %% % Use SPAMS Online Dictionary Learning Algorithm % to Learn overcomplete dictionary (Julien Mairal 2009) % Initialising Dictionary structure % Setting Dictionary structure fields (toolbox, name, param, D and time) % to zero values SMALL.DL(1)=SMALL_init_DL(); % Defining fields needed for dictionary learning SMALL.DL(1).toolbox = 'SPAMS'; SMALL.DL(1).name = 'mexTrainDL'; % Type 'help mexTrainDL in MATLAB prompt for explanation of parameters. SMALL.DL(1).param=struct(... 'D', SMALL.Problem.initdict,... 'K', SMALL.Problem.p,... 'lambda', lambda(i),... 'iter', 200,... 'mode', 3,... 'modeD', 0); % 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 Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain; maxatoms = floor(prod(SMALL.Problem.blocksize)/2); 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 results %% % This will show denoised image and dictionary for all lambdas. If you % are not interested to see it and do not want clutter your screen % comment following line SMALL_ImgDeNoiseResult(SMALL); time(1,i) = SMALL.DL(1).time; psnr(1,i) = SMALL.solver(1).reconstructed.psnr; clear SMALL end %% show time and psnr %% figure('Name', 'SPAMS LAMBDA TEST'); subplot(1,2,1); plot(lambda, time(1,:), 'ro-'); title('time vs lambda'); subplot(1,2,2); plot(lambda, psnr(1,:), 'b*-'); title('PSNR vs lambda');