Mercurial > hg > smallbox
view Problems/ImgDenoise_reconstruct.m @ 91:8a93741e416e
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author | Raja Giryes |
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date | Tue, 12 Apr 2011 16:45:24 +0300 |
parents | b9465d2bb3b0 |
children | 5b34af39bf9a 5b2ae0af72f9 |
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function reconstructed=ImgDenoise_reconstruct(y, Problem, SparseDict) %%% Pierre Villars Example - reconstruction function % % Centre for Digital Music, Queen Mary, University of London. % This file copyright 2009 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. % % This example is based on the experiment suggested by Professor Pierre % Vandergheynst on the SMALL meeting in Villars. % using sparse representation y in dictionary Problem.A reconstruct the % patches from the target image % stepsize % if (isfield(Problem,'stepsize')) stepsize = Problem.stepsize; if (numel(stepsize)==1) stepsize = ones(1,2)*stepsize; end else stepsize = ones(1,2); end if (any(stepsize<1)) error('Invalid step size.'); end % lambda % if (isfield(Problem,'lambda')) lambda = Problem.lambda; else lambda = Problem.maxval/(10*Problem.sigma); end if exist('SparseDict','var')&&(SparseDict==1) if issparse(Problem.A) A = Problem.A; else A = sparse(Problem.A); end cl_samp=add_dc(dictsep(Problem.basedict,A,y), Problem.b1dc,'columns'); else cl_samp=add_dc(Problem.A*y, Problem.b1dc,'columns'); end % combine the patches into reconstructed image cl_im=col2imstep(cl_samp, size(Problem.Noisy), Problem.blocksize); cnt = countcover(size(Problem.Noisy),Problem.blocksize,stepsize); im = (cl_im+lambda*Problem.Noisy)./(cnt + lambda); % y(y~=0)=1; % numD=sum(y,2); % nnzy=sum(y,1); % figure(200);plot(sort(numD)); % figure(201);plot(sort(nnzy)); [v.RMSErn, v.RMSEcd, v.rn_im, v.cd_im]=vmrse_type2(Problem.Original, Problem.Noisy, im); %% output structure image+psnr %% reconstructed.Image=im; reconstructed.psnr = 20*log10(Problem.maxval * sqrt(numel(Problem.Original(:))) / norm(Problem.Original(:)-im(:))); reconstructed.vmrse=v; reconstructed.ssim=ssim_index(Problem.Original, im); end