Mercurial > hg > smallbox
view Problems/generateImageDenoiseProblem.m @ 71:165e3b249170
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author | idamnjanovic |
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date | Wed, 16 Mar 2011 16:28:03 +0000 |
parents | 55faa9b5d1ac |
children | 8208316abec6 |
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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. % % 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: % - im - image matrix (if not present function promts user for an % image file) , % - trainnum - number of training samples (default - 40000) % - blocksize - block (patch) vertical/horizontal dimension (default 8), % - dictsize - dictionary size (default - 256), % - sigma - noise level (default - 20), % - noise gain (default - 1.15), % - maxval - maximum value (default - 255) % - initdict - initial dictionary (default - 4x overcomlete dct) % % The output of the function is stucture with following fields: % - name - name of the original image (if image is read inside of the % function) % - Original - original image matrix, % - Noisy - image with added noise, % - b - training patches, % - m - size of training patches (default 64), % - n - number of training patches, % - p - number of dictionary elements to be learned, % - blocksize - block size (default [8 8]), % - 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) % % Based on KSVD denoise demo by Ron Rubinstein % See also KSVDDENOISEDEMO and KSVDDEMO. % Ron Rubinstein % Computer Science Department % Technion, Haifa 32000 Israel % ronrubin@cs % August 2009 %% disp(' '); disp(' ********** Denoising Problem **********'); disp(' '); disp(' This function reads an image, adds random Gaussian noise,'); disp(' that can be later denoised by using dictionary learning techniques.'); disp(' '); %% prompt user for image %% %ask for file name FS=filesep; TMPpath=pwd; if ~ exist( 'im', 'var' ) || isempty(im) [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m')); cd([pathstr1,FS,'data',FS,'images']); [filename,pathname] = uigetfile({'*.png;'},'Select an image'); [pathstr, name, ext, versn] = fileparts(filename); data.name=name; im = imread(filename); %im = double(im); end; im = double(im); cd(TMPpath); %% check input parameters %% if ~ exist( 'blocksize', 'var' ) || isempty(blocksize),blocksize = 8;end if ~ exist( 'dictsize', 'var' ) || isempty(dictsize), dictsize = 256;end if ~ exist( 'trainnum', 'var' ) || isempty(trainnum),trainnum = 40000;end if ~ exist( 'sigma', 'var' ) || isempty(sigma), sigma = 20; end if ~ exist( 'gain', 'var' ) || isempty(gain), gain = 1.15; end if ~ exist( 'maxval', 'var' ) || isempty(maxval), maxval = 255; end if ~ exist( 'initdict', 'var' ) || isempty(initdict), initdict = 'odct'; end %% generate noisy image %% disp(' '); disp('Generating noisy image...'); n = randn(size(im)) * sigma; imnoise = im + n; %% set parameters %% x = imnoise; p = ndims(x); psnr=20*log10(maxval * sqrt(numel(im)) / norm(im(:)-imnoise(:))); if (p==2 && any(size(x)==1) && length(blocksize)==1) p = 1; end % blocksize % if (numel(blocksize)==1) blocksize = ones(1,p)*blocksize; end if (strcmpi(initdict,'odct')) initdict = odctndict(blocksize,dictsize,p); elseif (strcmpi(initdict,'data')) clear initdict; % causes initialization using random examples else error('Invalid initial dictionary specified.'); end if exist( 'initdict', 'var' ) initdict = initdict(:,1:dictsize); end %%%% create training data %%% ids = cell(p,1); if (p==1) ids{1} = reggrid(length(x)-blocksize+1, trainnum, 'eqdist'); else [ids{:}] = reggrid(size(x)-blocksize+1, trainnum, 'eqdist'); end X = sampgrid(x,blocksize,ids{:}); % remove dc in blocks to conserve memory % bsize = 2000; for i = 1:bsize:size(X,2) 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'); [b1, dc] = remove_dc(xcol,'columns'); %% output structure %% data.Original = im; data.Noisy = imnoise; data.noisy_psnr=psnr; data.b = X; data.b1=b1; data.b1dc=dc; data.m = size(X,1); data.n = size(X,2); data.p = dictsize; data.blocksize=blocksize; data.sigma = sigma; data.gain = gain; data.maxval = maxval; data.initdict= initdict; data.signalDim=2; data.sparse=1; end %% end of function