ivan@136: %%  Audio Declipping Example
ivan@136: %   
ivan@136: %   CHANGE!!! This example is based on the experiment suggested by Professor Pierre
ivan@136: %   Vandergheynst on the SMALL meeting in Villars.
ivan@136: %   The idea behind is to use patches from source image as a dictionary in
ivan@136: %   which we represent target image using matching pursuit algorithm.
ivan@136: %   Calling Pierre_Problem function to get src image to be used as dictionary
ivan@136: %   and target image to be represented using MP with 3 patches from source image
ivan@136: 
ivan@136: %
ivan@136: %   Centre for Digital Music, Queen Mary, University of London.
ivan@136: %   This file copyright 2011 Ivan Damnjanovic.
ivan@136: %
ivan@136: %   This program is free software; you can redistribute it and/or
ivan@136: %   modify it under the terms of the GNU General Public License as
ivan@136: %   published by the Free Software Foundation; either version 2 of the
ivan@136: %   License, or (at your option) any later version.  See the file
ivan@136: %   COPYING included with this distribution for more information.
ivan@136: %
ivan@136: %%
ivan@136: 
ivan@136: clear all;
ivan@136: 
ivan@136: %   Defining the Problem structure
ivan@136: 
ivan@136: SMALL.Problem = generateAudioDeclippingProblem('male01_8kHz.wav', 0.6, 256, 0.5, @wRect, @wSine, @wRect, @Gabor_Dictionary, 2);
ivan@136: 
ivan@136: % %   Show original image and image that is used as a dictionary
ivan@136: % figure('Name', 'Original and Dictionary Image');
ivan@136: % 
ivan@136: % subplot(1,2,1); imagesc(SMALL.Problem.imageTrg/SMALL.Problem.maxval);
ivan@136: % title('Original Image');colormap(gray);axis off; axis image;
ivan@136: % subplot(1,2,2); imagesc(SMALL.Problem.imageSrc/SMALL.Problem.maxval);
ivan@136: % title('Dictionary image:');colormap(gray);axis off; axis image;
ivan@136: time=0;
ivan@136: coeffFrames = zeros(SMALL.Problem.p, SMALL.Problem.n);
ivan@136: 
ivan@136: for i=1:SMALL.Problem.n
ivan@136:     
ivan@136:     idx = find(SMALL.Problem.M(:,i));
ivan@136:     SMALL.Problem.A  = SMALL.Problem.B(idx,:);
ivan@136:     
ivan@136:     SMALL.Problem.b1 = SMALL.Problem.b(idx,i);
ivan@136:     
ivan@136:    
ivan@136:     
ivan@136:     %   Defining the parameters sparse representation
ivan@136:     SMALL.solver=SMALL_init_solver;
ivan@136:     SMALL.solver.toolbox='ompbox';
ivan@136:     SMALL.solver.name='omp2';
ivan@136:     
ivan@136:     SMALL.solver.param=struct(...
ivan@136:         'epsilon', 0.001,...
ivan@136:         'maxatoms', 64); 
ivan@136:     
ivan@136:     % Find solution
ivan@136:     
ivan@136:     SMALL.solver=SMALL_solve(SMALL.Problem, SMALL.solver);
ivan@136:     
ivan@136:     
ivan@136:     coeffFrames(:,i) = SMALL.solver.solution;
ivan@136:     time = time + SMALL.solver.time;
ivan@136:     
ivan@136:     
ivan@136:       
ivan@136: end
ivan@136: 
ivan@136: %%   Set reconstruction function
ivan@136: 
ivan@136: SMALL.Problem.reconstruct=@(x) AudioDeclipping_reconstruct(x, SMALL.Problem);
ivan@136: reconstructed=SMALL.Problem.reconstruct(coeffFrames);
ivan@136: 
ivan@136: %%  plot time and psnr given dictionary size %%
ivan@136: figure('Name', 'time and psnr');
ivan@136: 
ivan@136: subplot(1,2,1); plot(dictsize(1,:), time(1,:), 'ro-');
ivan@136: title('Time vs number of source image patches used');
ivan@136: subplot(1,2,2); plot(dictsize(1,:), psnr(1,:), 'b*-');
ivan@136: title('PSNR vs number of source image patches used');