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changeset 120:5a20f4936159 sup_158_IMG_Processing_toolbox_

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Merge from branch "sup_163_ssim_IMG_T_dependeny"
author Ivan Damnjanovic lnx <ivan.damnjanovic@eecs.qmul.ac.uk>
date Tue, 24 May 2011 16:18:21 +0100
parents ceb81fd882aa (diff) 5356a6e13a25 (current diff)
children 38d4fbf6ae24
files Problems/ImgDenoise_reconstruct.m
diffstat 7 files changed, 169 insertions(+), 244 deletions(-) [+]
line wrap: on
line diff
--- a/DL/RLS-DLA/SMALL_rlsdla.m	Tue May 24 16:16:36 2011 +0100
+++ b/DL/RLS-DLA/SMALL_rlsdla.m	Tue May 24 16:18:21 2011 +0100
@@ -133,8 +133,8 @@
 end
 
 if (show_dictionary)
-    dictimg = showdict(D,[8 8],round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
-    figure(2); imshow(imresize(dictimg,2,'nearest'));
+    dictimg = SMALL_showdict(D,[8 8],round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
+    figure(2); imagesc(dictimg);colormap(gray);axis off; axis image;
 end
 % Forgetting factor
 
@@ -193,9 +193,9 @@
    
    C = C - (alfa * u)* u';
    if (show_dictionary &&(mod(i,show_iter)==0))
-       dictimg = showdict(D,[8 8],...
+       dictimg = SMALL_showdict(D,[8 8],...
             round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');  
-       figure(2); imshow(imresize(dictimg,2,'nearest'));
+       figure(2); imagesc(dictimg);colormap(gray);axis off; axis image;
        pause(0.02);
    end
 end
--- a/Problems/ImgDenoise_reconstruct.m	Tue May 24 16:16:36 2011 +0100
+++ b/Problems/ImgDenoise_reconstruct.m	Tue May 24 16:18:21 2011 +0100
@@ -56,10 +56,10 @@
 % 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);
+[v.RMSErn, v.RMSEcd, v.rn_im, v.cd_im]=SMALL_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);
+reconstructed.ssim=SMALL_ssim_index(Problem.Original, im);
 end
\ No newline at end of file
--- a/util/SMALL_ImgDeNoiseResult.m	Tue May 24 16:16:36 2011 +0100
+++ b/util/SMALL_ImgDeNoiseResult.m	Tue May 24 16:18:21 2011 +0100
@@ -37,7 +37,7 @@
     else
         D = SMALL.DL(i-1).D;
     end
-    dictimg = showdict(D,SMALL.Problem.blocksize,...
+    dictimg = SMALL_showdict(D,SMALL.Problem.blocksize,...
         round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
     
     subplot(2,m,m+i);imagesc(dictimg);axis off; axis image; 
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/util/SMALL_showdict.m	Tue May 24 16:18:21 2011 +0100
@@ -0,0 +1,119 @@
+function x = SMALL_showdict(D,sz,n,m,varargin)
+%% SMALL_SHOWDICT Display a dictionary of image patches.
+%%  Reimplementation of showdict function from KSVD toolbox with Image
+%%  Processing toolbox dependecies removed
+%
+%  SMALL_SHOWDICT(D,SZ,N,M) displays the contents of the dictionary D, whos
+%  columns are 2-D image patches (in column-major order). SZ = [SX SY] is
+%  the size of the image patches. SHOWDICT displays the atoms on an N x M
+%  grid. If there are more atoms in D then only the first N*M are
+%  displayed.
+%
+%  SMALL_SHOWDICT(...,'lines') separates the dictionary atoms by black lines.
+%  SMALL_SHOWDICT(...,'whitelines') separates the dictionary atoms by white
+%  lines.
+%
+%  SMALL_SHOWDICT(...,'linewidth',W) when used with either 'lines' or
+%  'whitelines' sets the width of the lines to W pixels (default=1).
+%
+%  SMALL_SHOWDICT(...,'highcontrast') increases the contrast of the figure by
+%  normalizing the intensity values of each atom individually to the range
+%  of [0,1] (the default behavior is to normalize the values of the entire
+%  figure to [0,1] as one image). Note that in this way, the relative
+%  intensities of the atoms are not maintained.
+%
+%  X = SMALL_SHOWDICT(...) returns a bitmat of the dictionary image without
+%  displaying the figure.
+
+
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2011 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.
+%   
+%%
+
+if (size(D,2) < n*m)
+  D = [D zeros(size(D,1),n*m-size(D,2))];
+end
+
+
+%%%  parse input arguments  %%%
+
+linewidth = 1;
+highcontrast = 0;
+drawlines = 0;
+linecolor = 0;
+
+for i = 1:length(varargin)
+  if (~ischar(varargin{i}))
+    continue;
+  end
+  switch(varargin{i})
+    case 'highcontrast'
+      highcontrast = 1;
+    case 'lines'
+      drawlines = 1;
+    case 'whitelines'
+      drawlines = 1;
+      linecolor = 1;
+    case 'linewidth'
+      linewidth = varargin{i+1};
+  end
+end
+
+
+
+%%%  create dictionary image  %%%
+
+
+if (drawlines)
+  
+  D = [D ; nan(sz(1)*linewidth,size(D,2))];
+  sz(2) = sz(2)+linewidth;
+  x = col2imstep(D(:,1:n*m),[n m].*sz, sz, sz);
+  sz = [sz(2) sz(1)];
+  D = im2colstep(x',sz, sz);
+  D = [D ; nan(sz(1)*linewidth,size(D,2))];
+  sz(2) = sz(2)+linewidth;
+  x = col2imstep(D(:,1:n*m),[m n].*sz,sz,sz);
+  x = x';
+  x = x(1:end-linewidth,1:end-linewidth);
+  
+  if (highcontrast)
+    for i = 0:n-1
+      for j = 0:m-1
+        x(i*sz(1)+1:i*sz(1)+sz(1)-linewidth, j*sz(2)+1:j*sz(2)+sz(2)-linewidth) = ...
+          imnormalize(x(i*sz(1)+1:i*sz(1)+sz(1)-linewidth, j*sz(2)+1:j*sz(2)+sz(2)-linewidth));
+      end
+    end
+  else
+    x = imnormalize(x);
+  end
+  
+  x(isnan(x)) = linecolor;
+  
+else
+  
+  x = col2imstep(D(:,1:n*m),[n m].*sz, sz, sz);
+  
+  if (highcontrast)
+    for i = 0:n-1
+      for j = 0:m-1
+        x(i*sz(1)+1:i*sz(1)+sz(1), j*sz(2)+1:j*sz(2)+sz(2)) = ...
+          imnormalize(x(i*sz(1)+1:i*sz(1)+sz(1), j*sz(2)+1:j*sz(2)+sz(2)));
+      end
+    end
+  else
+    x = imnormalize(x);
+  end
+end
+
+
+if (nargout==0)
+    imagesc(dictimg);colormap(gray);axis off; axis image; 
+end
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/util/SMALL_vmrse_type2.m	Tue May 24 16:18:21 2011 +0100
@@ -0,0 +1,43 @@
+function [RMSErn, RMSEcd, rn_im, cd_im] = SMALL_vmrse_type2(orig, corr, recon)
+%%   Implementation of VectorRMSE type2
+%
+% 
+%   Input:
+%       - Original image
+%       - Corrupted image
+%       - Reconstructed Image
+%
+%   Output:
+%       - RMSErn - RMSE from residual noise (noise not completely removed)
+%       - RMSEcd - RMSE from collateral distortion - excessive filtering
+%       - rn_im  - image of residual noise
+%       - cd_im  - image of collateral distortion
+%
+%   F. Russo, "New Method for Performance Evaluation of Grayscale Image
+%   Denoising filters", IEEE Signal Processing Letters, vol. 17, no. 5,
+%   pp.417-420, May 2010
+
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2011 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.
+%%
+
+    recon_int = round(recon);
+    
+    RN1 = ((orig<recon_int)&(recon_int<=corr));
+    RN2 = ((orig>recon_int)&(recon_int>=corr));
+    CD1 = ((orig<recon_int)&(recon_int>corr));
+    CD2 = ((orig>recon_int)&(recon_int<corr));
+    
+    RMSErn = sqrt(sum(sum((RN1+RN2).*(orig-recon).^2)))/512;
+    RMSEcd = sqrt(sum(sum((CD1+CD2).*(orig-recon).^2)))/512;
+    rn_im=RN1+RN2;
+    cd_im=CD1+CD2;
+    
+end
+
--- a/util/ssim_index.m	Tue May 24 16:16:36 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,194 +0,0 @@
-function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L)
-
-%========================================================================
-%SSIM Index, Version 1.0
-%Copyright(c) 2003 Zhou Wang
-%All Rights Reserved.
-%
-%The author is with Howard Hughes Medical Institute, and Laboratory
-%for Computational Vision at Center for Neural Science and Courant
-%Institute of Mathematical Sciences, New York University.
-%
-%----------------------------------------------------------------------
-%Permission to use, copy, or modify this software and its documentation
-%for educational and research purposes only and without fee is hereby
-%granted, provided that this copyright notice and the original authors'
-%names appear on all copies and supporting documentation. This program
-%shall not be used, rewritten, or adapted as the basis of a commercial
-%software or hardware product without first obtaining permission of the
-%authors. The authors make no representations about the suitability of
-%this software for any purpose. It is provided "as is" without express
-%or implied warranty.
-%----------------------------------------------------------------------
-%
-%This is an implementation of the algorithm for calculating the
-%Structural SIMilarity (SSIM) index between two images. Please refer
-%to the following paper:
-%
-%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image
-%quality assessment: From error measurement to structural similarity"
-%IEEE Transactios on Image Processing, vol. 13, no. 1, Jan. 2004.
-%
-%Kindly report any suggestions or corrections to zhouwang@ieee.org
-%
-%----------------------------------------------------------------------
-%
-%Input : (1) img1: the first image being compared
-%        (2) img2: the second image being compared
-%        (3) K: constants in the SSIM index formula (see the above
-%            reference). defualt value: K = [0.01 0.03]
-%        (4) window: local window for statistics (see the above
-%            reference). default widnow is Gaussian given by
-%            window = fspecial('gaussian', 11, 1.5);
-%        (5) L: dynamic range of the images. default: L = 255
-%
-%Output: (1) mssim: the mean SSIM index value between 2 images.
-%            If one of the images being compared is regarded as 
-%            perfect quality, then mssim can be considered as the
-%            quality measure of the other image.
-%            If img1 = img2, then mssim = 1.
-%        (2) ssim_map: the SSIM index map of the test image. The map
-%            has a smaller size than the input images. The actual size:
-%            size(img1) - size(window) + 1.
-%
-%Default Usage:
-%   Given 2 test images img1 and img2, whose dynamic range is 0-255
-%
-%   [mssim ssim_map] = ssim_index(img1, img2);
-%
-%Advanced Usage:
-%   User defined parameters. For example
-%
-%   K = [0.05 0.05];
-%   window = ones(8);
-%   L = 100;
-%   [mssim ssim_map] = ssim_index(img1, img2, K, window, L);
-%
-%See the results:
-%
-%   mssim                        %Gives the mssim value
-%   imshow(max(0, ssim_map).^4)  %Shows the SSIM index map
-%
-%========================================================================
-
-
-if (nargin < 2 || nargin > 5)
-   ssim_index = -Inf;
-   ssim_map = -Inf;
-   return;
-end
-
-if (size(img1) ~= size(img2))
-   ssim_index = -Inf;
-   ssim_map = -Inf;
-   return;
-end
-
-[M N] = size(img1);
-
-if (nargin == 2)
-   if ((M < 11) || (N < 11))
-	   ssim_index = -Inf;
-	   ssim_map = -Inf;
-      return
-   end
-   window = fspecial('gaussian', 11, 1.5);	%
-   K(1) = 0.01;								      % default settings
-   K(2) = 0.03;								      %
-   L = 255;                                  %
-end
-
-if (nargin == 3)
-   if ((M < 11) || (N < 11))
-	   ssim_index = -Inf;
-	   ssim_map = -Inf;
-      return
-   end
-   window = fspecial('gaussian', 11, 1.5);
-   L = 255;
-   if (length(K) == 2)
-      if (K(1) < 0 || K(2) < 0)
-		   ssim_index = -Inf;
-   		ssim_map = -Inf;
-	   	return;
-      end
-   else
-	   ssim_index = -Inf;
-   	ssim_map = -Inf;
-	   return;
-   end
-end
-
-if (nargin == 4)
-   [H W] = size(window);
-   if ((H*W) < 4 || (H > M) || (W > N))
-	   ssim_index = -Inf;
-	   ssim_map = -Inf;
-      return
-   end
-   L = 255;
-   if (length(K) == 2)
-      if (K(1) < 0 || K(2) < 0)
-		   ssim_index = -Inf;
-   		ssim_map = -Inf;
-	   	return;
-      end
-   else
-	   ssim_index = -Inf;
-   	ssim_map = -Inf;
-	   return;
-   end
-end
-
-if (nargin == 5)
-   [H W] = size(window);
-   if ((H*W) < 4 || (H > M) || (W > N))
-	   ssim_index = -Inf;
-	   ssim_map = -Inf;
-      return
-   end
-   if (length(K) == 2)
-      if (K(1) < 0 || K(2) < 0)
-		   ssim_index = -Inf;
-   		ssim_map = -Inf;
-	   	return;
-      end
-   else
-	   ssim_index = -Inf;
-   	ssim_map = -Inf;
-	   return;
-   end
-end
-
-C1 = (K(1)*L)^2;
-C2 = (K(2)*L)^2;
-window = window/sum(sum(window));
-img1 = double(img1);
-img2 = double(img2);
-
-mu1   = filter2(window, img1, 'valid');
-mu2   = filter2(window, img2, 'valid');
-mu1_sq = mu1.*mu1;
-mu2_sq = mu2.*mu2;
-mu1_mu2 = mu1.*mu2;
-sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq;
-sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq;
-sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2;
-
-if (C1 > 0 & C2 > 0)
-   ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2));
-else
-   numerator1 = 2*mu1_mu2 + C1;
-   numerator2 = 2*sigma12 + C2;
-	denominator1 = mu1_sq + mu2_sq + C1;
-   denominator2 = sigma1_sq + sigma2_sq + C2;
-   ssim_map = ones(size(mu1));
-   index = (denominator1.*denominator2 > 0);
-   ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index));
-   index = (denominator1 ~= 0) & (denominator2 == 0);
-   ssim_map(index) = numerator1(index)./denominator1(index);
-end
-
-mssim = mean2(ssim_map);
-
-return
\ No newline at end of file
--- a/util/vmrse_type2.m	Tue May 24 16:16:36 2011 +0100
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,43 +0,0 @@
-function [RMSErn, RMSEcd, rn_im, cd_im] = vmrse_type2(orig, corr, recon)
-
-%%%   Implementation of VectorRMSE type2
-%
-%   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2011 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.
-% 
-%   Input:
-%       - Original image
-%       - Corrupted image
-%       - Reconstructed Image
-%
-%   Output:
-%       - RMSErn - RMSE from residual noise (noise not completely removed)
-%       - RMSEcd - RMSE from collateral distortion - excessive filtering
-%       - rn_im  - image of residual noise
-%       - cd_im  - image of collateral distortion
-%
-%   F. Russo, "New Method for Performance Evaluation of Grayscale Image
-%   Denoising filters", IEEE Signal Processing Letters, vol. 17, no. 5,
-%   pp.417-420, May 2010
-%%
-
-    recon_int = round(recon);
-    
-    RN1 = ((orig<recon_int)&(recon_int<=corr));
-    RN2 = ((orig>recon_int)&(recon_int>=corr));
-    CD1 = ((orig<recon_int)&(recon_int>corr));
-    CD2 = ((orig>recon_int)&(recon_int<corr));
-    
-    RMSErn = sqrt(sum(sum((RN1+RN2).*(orig-recon).^2)))/512;
-    RMSEcd = sqrt(sum(sum((CD1+CD2).*(orig-recon).^2)))/512;
-    rn_im=RN1+RN2;
-    cd_im=CD1+CD2;
-    
-end
-