changeset 43:984c3c175be2

(none)

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_dic_ODCT_solvers_OMP_BPDN_etc_test.m	Mon Mar 14 15:41:59 2011 +0000
@@ -0,0 +1,163 @@
+%% DICTIONARY LEARNING FOR IMAGE DENOISING
+%   This file contains an example of how SMALLbox can be used to test different
+%   dictionary learning techniques in Image Denoising problem.
+%   It calls generateImageDenoiseProblem that will let you to choose image,
+%   add noise and use noisy image to generate training set for dictionary
+%   learning.
+%   Three dictionary learning techniques were compared:
+%   -   KSVD - M. Elad, R. Rubinstein, and M. Zibulevsky, "Efficient
+%              Implementation of the K-SVD Algorithm using Batch Orthogonal
+%              Matching Pursuit", Technical Report - CS, Technion, April 2008.
+%   -   KSVDS - R. Rubinstein, M. Zibulevsky, and M. Elad, "Learning Sparse
+%               Dictionaries for Sparse Signal Approximation", Technical
+%               Report - CS, Technion, June 2009.
+%   -   SPAMS - J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online
+%               Dictionary Learning for Sparse Coding. International
+%               Conference on Machine Learning,Montreal, Canada, 2009
+%
+%
+% Ivan Damnjanovic 2010
+%%
+
+clear;
+
+%   If you want to load the image outside of generateImageDenoiseProblem
+%   function uncomment following lines. This can be useful if you want to
+%   denoise more then one image for example.
+
+% TMPpath=pwd;
+% FS=filesep;
+% [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);
+% test_image = imread(filename);
+% test_image = double(test_image);
+% cd(TMPpath);
+% SMALL.Problem.name=name;
+
+
+% Defining Image Denoising Problem as Dictionary Learning
+% Problem. As an input we set the number of training patches.
+
+SMALL.Problem = generateImageDenoiseProblem('', 40000, '','', 20);
+
+Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
+maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
+%%   Use KSVD Dictionary Learning Algorithm to Learn overcomplete dictionary
+% 
+% %   Initialising Dictionary structure
+% %   Setting Dictionary structure fields (toolbox, name, param, D and time)
+% %   to zero values
+% 
+% SMALL.DL(1)=SMALL_init_DL();
+% 
+% % Defining the parameters needed for dictionary learning
+% 
+% SMALL.DL(1).toolbox = 'KSVD';
+% SMALL.DL(1).name = 'ksvd';
+% 
+% %   Defining the parameters for KSVD
+% %   In this example we are learning 256 atoms in 20 iterations, so that
+% %   every patch in the training set can be represented with target error in
+% %   L2-norm (EData)
+% %   Type help ksvd in MATLAB prompt for more options.
+% 
+% 
+% SMALL.DL(1).param=struct(...
+%     'Edata', Edata,...
+%     'initdict', SMALL.Problem.initdict,...
+%     'dictsize', SMALL.Problem.p,...
+%     'iternum', 20,...
+%     'memusage', 'high');
+% 
+% %   Learn the dictionary
+% 
+% SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));
+%%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values 
+% 
+
+SMALL.DL(1)=SMALL_init_DL();
+%   Take initial dictonary (overcomplete DCT) to be a final dictionary for
+%   reconstruction
+
+SMALL.DL(1).D=SMALL.Problem.initdict;
+%%
+
+%   Set SMALL.Problem.A dictionary
+%   (backward compatiblity with SPARCO: solver structure communicate
+%   only with Problem structure, ie no direct communication between DL and
+%   solver structures)
+SMALL.Problem.A = SMALL.DL(1).D;
+
+SparseDict=0;
+SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem, SparseDict);
+
+%%
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+
+SMALL.solver(1)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(1).toolbox='ompbox';
+SMALL.solver(1).name='omp2';
+SMALL.solver(1).param=struct(...
+    'epsilon',Edata,...
+    'maxatoms', maxatoms); 
+
+%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+%   but backward compatible with KSVD definition of denoising
+%   Pay attention that since implicit base dictionary is used, denoising
+%   can be much faster then using explicit dictionary in KSVD example.
+
+SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
+
+%%
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+lam=2*SMALL.Problem.sigma;%*sqrt(2*log2(size(SMALL.Problem.A,1)))
+for i=1:11
+    lambda(i)=lam+5-(i-1);
+SMALL.DL(2)=SMALL_init_DL();
+i
+%SMALL.Problem.A = SMALL.Problem.initdict;
+SMALL.DL(2).D=SMALL.Problem.initdict;
+SMALL.solver(2)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(2).toolbox='SPAMS';
+SMALL.solver(2).name='mexLasso';
+SMALL.solver(2).param=struct(...
+    'mode', 2, ...
+     'lambda',lambda(i),...
+    'L', maxatoms); 
+
+%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+%   but backward compatible with KSVD definition of denoising
+%   Pay attention that since implicit base dictionary is used, denoising
+%   can be much faster then using explicit dictionary in KSVD example.
+
+SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+
+
+% show results %
+
+%SMALL_ImgDeNoiseResult(SMALL);
+
+    time(1,i) = SMALL.solver(2).time;
+    psnr(1,i) = SMALL.solver(2).reconstructed.psnr;
+end%% show time and psnr %%
+figure('Name', 'SPAMS LAMBDA TEST');
+
+subplot(1,2,1); plot(lambda, time(1,:), 'ro-');
+title('time vs lambda');
+subplot(1,2,2); plot(lambda, psnr(1,:), 'b*-');
+title('PSNR vs lambda');
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