Mercurial > hg > smallbox
view examples/Pierre Villars/Pierre_Villars_Example.m @ 214:b9b4dc87f1aa luisf_dev
Additional help comments in ~/DL/Majorization Minimization DL/wrapper_mm_DL.m.
author | Aris Gretsistas <aris.gretsistas@elec.qmul.ac.uk> |
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date | Wed, 21 Mar 2012 18:27:23 +0000 |
parents | f42aa8bcb82f |
children | 8b3c71bb44eb |
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%% Pierre Villars Example % % This example is based on the experiment suggested by Professor Pierre % Vandergheynst on the SMALL meeting in Villars. % The idea behind is to use patches from source image as a dictionary in % which we represent target image using matching pursuit algorithm. % Calling Pierre_Problem function to get src image to be used as dictionary % and target image to be represented using MP with 3 patches from source image % % 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; % Defining the Problem structure SMALL.Problem = generatePierreProblem(); % Show original image and image that is used as a dictionary figure('Name', 'Original and Dictionary Image'); subplot(1,2,1); imagesc(SMALL.Problem.imageTrg/SMALL.Problem.maxval); title('Original Image');colormap(gray);axis off; axis image; subplot(1,2,2); imagesc(SMALL.Problem.imageSrc/SMALL.Problem.maxval); title('Dictionary image:');colormap(gray);axis off; axis image; % Using ten different dictionary sizes. First dictionary will contain all % patches from the source image and last one will have only % num_src_patches/2^9 atoms representing equidistant patches taken from % the source image. n =10; dictsize=zeros(1,n); time = zeros(1,n); psnr = zeros(1,n); for i=1:n % Set reconstruction function SMALL.Problem.reconstruct=@(x) Pierre_reconstruct(x, SMALL.Problem); % Defining the parameters sparse representation SMALL.solver(i)=SMALL_init_solver; SMALL.solver(i).toolbox='SMALL'; SMALL.solver(i).name='SMALL_MP'; % Parameters needed for matching pursuit (max number of atoms is 3 % and residual error goal is 1e-14 SMALL.solver(i).param=sprintf('%d, 1e-14',3); % Represent the image using the source image patches as dictionary SMALL.solver(i)=SMALL_solve(SMALL.Problem, SMALL.solver(i)); dictsize(1,i) = size(SMALL.Problem.A,2); time(1,i) = SMALL.solver(i).time; psnr(1,i) = SMALL.solver(i).reconstructed.psnr; % Set new SMALL.Problem.A dictionary taking every second patch from % previous dictionary SMALL.Problem.A=SMALL.Problem.A(:,1:2:dictsize(1,i)); %% show reconstructed image %% figure('Name', sprintf('dictsize=%d', dictsize(1,i))); imagesc(SMALL.solver(i).reconstructed.image/SMALL.Problem.maxval); title(sprintf('Reconstructed image, PSNR: %.2f dB in %.2f s',... SMALL.solver(i).reconstructed.psnr, SMALL.solver(i).time )); colormap(gray);axis off; axis image; end %% plot time and psnr given dictionary size %% figure('Name', 'time and psnr'); subplot(1,2,1); plot(dictsize(1,:), time(1,:), 'ro-'); title('Time vs number of source image patches used'); subplot(1,2,2); plot(dictsize(1,:), psnr(1,:), 'b*-'); title('PSNR vs number of source image patches used');