# HG changeset patch
# User idamnjanovic
# Date 1300117372 0
# Node ID b9465d2bb3b02133718fc5ad73ab035d29ab8f69
# Parent  2c59257d734c9b18a1b4bca0656ce2f41e597ca8


diff -r 2c59257d734c -r b9465d2bb3b0 Problems/ImgDenoise_reconstruct.m
--- /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
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