comparison Problems/generateImageDenoiseProblem.m @ 126:db5a7fe1a404 ivand_dev

Merge from branch "sup_158_IMG_Processing_toolbox_"

 function data=generateImageDenoiseProblem(im, trainnum, blocksize, dictsize, sigma, gain, maxval, initdict);
-%%% Generate Image Denoising Problem
-%
-%   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2010 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.
+%% Generate Image Denoising Problem
 %   
 %   generateImageDenoiseProblem is a part of the SMALLbox and generates
 %   a problem that can be used for comparison of Dictionary Learning/Sparse
 %   Representation techniques in image denoising scenario.
 %   The function takes as an input:

 %   -   sigma - noise level,
 %   -   noise gain (default - 1.15),
 %   -   maxval - maximum value (default - 255)
 %   -   initdict - initial dictionary (default - 4x overcomlete dct)
 %   -   signalDim - signal dimension (default - 2)
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2010 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.
 %
 %   Based on KSVD denoise demo by Ron Rubinstein
 %   See also KSVDDENOISEDEMO and KSVDDEMO.
 %   Ron Rubinstein
 %   Computer Science Department

     blockids = i : min(i+bsize-1,size(X,2));
     X(:,blockids) = remove_dc(X(:,blockids),'columns');
 end
 
 %  Noisy image blocks 
-xcol=im2col(x,blocksize,'sliding');
+xcol=im2colstep(x,blocksize);
 [b1, dc] = remove_dc(xcol,'columns');
 
 %% output structure %%
 
 data.Original = im;