comparison Problems/generateImageDenoiseProblem.m @ 10:207a6ae9a76f version1.0

(none)

+function data=generateImageDenoiseProblem(im, trainnum, blocksize, dictsize, sigma, gain, maxval, initdict);
+%%% Generate Image Denoising Problem
+%   Ivan Damnjanovic 2010
+%
+%   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 a file containin pre-calculated notes');
+    [pathstr, name, ext, versn] = fileparts(filename);
+    data.name=name;
+    im = imread(filename);
+    im = double(im);
+end;
+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);
+
+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
+
+%% output structure %%
+
+data.Original = im;
+data.Noisy = imnoise;
+data.b = X;
+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;
+
+end %% end of function