Mercurial > hg > smallbox
changeset 45:b9465d2bb3b0
(none)
author | idamnjanovic |
---|---|
date | Mon, 14 Mar 2011 15:42:52 +0000 |
parents | 2c59257d734c |
children | 6a37442514c5 |
files | Problems/ImgDenoise_reconstruct.m |
diffstat | 1 files changed, 65 insertions(+), 0 deletions(-) [+] |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/Problems/ImgDenoise_reconstruct.m Mon Mar 14 15:42:52 2011 +0000 @@ -0,0 +1,65 @@ +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 \ No newline at end of file