diff examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m @ 42:623fcf3a69b1

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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Mon Mar 14 15:41:53 2011 +0000
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+%% 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
+%%
+
+
+
+%   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.
+clear;
+TMPpath=pwd;
+FS=filesep;
+[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+cd([pathstr1,FS,'data',FS,'images']);
+load('test_image.mat');
+% [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;
+
+noise_level=[10 20 25 50 100];
+% Defining Image Denoising Problem as Dictionary Learning
+% Problem. As an input we set the number of training patches.
+for noise_ind=1:1
+for im_num=4:4
+SMALL.Problem = generateImageDenoiseProblem(test_image(im_num).i, 40000, '',512, noise_level(noise_ind));
+SMALL.Problem.name=im_num;
+
+results(noise_ind,im_num).noisy_psnr=SMALL.Problem.noisy_psnr;
+
+%%
+%   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.
+
+Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
+maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
+SMALL.DL(1).param=struct(...
+    'Edata', Edata,...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'exact', 1, ...
+    'iternum', 20,...
+    'memusage', 'high');
+
+%   Learn the dictionary
+
+SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));
+
+%   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;
+SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+
+%%
+%   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
+
+SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
+SMALL.solver(1).reconstructed.psnr
+%%
+% Use KSVDS Dictionary Learning Algorithm to denoise image
+
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+% 
+% SMALL.DL(2)=SMALL_init_DL();
+% 
+% % Defining the parameters needed for dictionary learning
+% 
+% SMALL.DL(2).toolbox = 'KSVDS';
+% SMALL.DL(2).name = 'ksvds';
+% 
+% %   Defining the parameters for KSVDS
+% %   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 (EDataS). We also impose "double sparsity" - dictionary itself
+% %   has to be sparse in the given base dictionary (Tdict - number of
+% %   nonzero elements per atom).
+% %   Type help ksvds in MATLAB prompt for more options.
+% 
+% 
+% SMALL.DL(2).param=struct(...
+%     'Edata', Edata, ...
+%     'Tdict', 6,...
+%     'stepsize', 1,...
+%     'dictsize', SMALL.Problem.p,...
+%     'iternum', 20,...
+%     'memusage', 'high');
+% SMALL.DL(2).param.initA = speye(SMALL.Problem.p);
+% SMALL.DL(2).param.basedict{1} = odctdict(8,16);
+% SMALL.DL(2).param.basedict{2} = odctdict(8,16);
+% 
+% % Learn the dictionary
+% 
+% SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
+
+%   Set SMALL.Problem.A dictionary and SMALL.Problem.basedictionary
+%   (backward compatiblity with SPARCO: solver structure communicate
+%   only with Problem structure, ie no direct communication between DL and
+%   solver structures)
+
+SMALL.Problem.A = SMALL.Problem.initdict;
+% SMALL.Problem.basedict{1} = SMALL.DL(2).param.basedict{1};
+% SMALL.Problem.basedict{2} = SMALL.DL(2).param.basedict{2};
+SMALL.DL(2).D=SMALL.Problem.initdict;
+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(2)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(2).toolbox='ompbox';
+SMALL.solver(2).name='omp2';
+SMALL.solver(2).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(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+%%
+
+for i =1:1
+
+X=SMALL.Problem.b1;
+X_norm=sqrt(sum(X.^2, 1));
+[X_norm_sort, p]=sort(X_norm);
+p1=p(X_norm_sort>Edata);
+if size(p1,2)>140000
+    p2 = randperm(size(p1,2));
+    p2=sort(p2(1:40000));
+    size(p2,2)
+    SMALL.Problem.b=X(:,p1(p2));
+else 
+    size(p1,2)
+    SMALL.Problem.b=X(:,p1);
+
+end
+
+lambda=0.9998
+
+%   Use Recursive Least Squares
+%   to Learn overcomplete dictionary 
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values
+
+SMALL.DL(3)=SMALL_init_DL();
+
+%   Defining fields needed for dictionary learning
+
+SMALL.DL(3).toolbox = 'SMALL';
+SMALL.DL(3).name = 'SMALL_rlsdla';
+SMALL.DL(3).param=struct(...
+    'Edata', Edata,...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'forgettingMode', 'FIX',...
+    'forgettingFactor', lambda);
+
+% %   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
+% 
+% SMALL.DL(3).param=struct(...
+%     'D', SMALL.Problem.initdict,...
+%     'K', SMALL.Problem.p,...
+%     'lambda', 2,...
+%     'iter', 200,...
+%     'mode', 3, ...
+%     'modeD', 0);
+
+%   Learn the dictionary
+
+SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3));
+%SMALL.DL(3).D(:,1)=SMALL.DL(1).D(:,1);
+% 
+% %   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)
+% 
+% 
+% 
+% %%
+% %   Initialising solver structure
+% %   Setting solver structure fields (toolbox, name, param, solution,
+% %   reconstructed and time) to zero values
+% SMALL.Problem.A = SMALL.DL(1).D;
+% SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+% maxatoms=5;
+% SMALL.solver(3)=SMALL_init_solver;
+% 
+% % Defining the parameters needed for denoising
+% 
+% % SMALL.solver(3).toolbox='SPAMS';
+% % SMALL.solver(3).name='mexLasso';
+% % SMALL.solver(3).param=struct(...
+% %     'mode', 1, ...
+% %     'lambda',Edata*Edata,...
+% %     'L', maxatoms); 
+% % %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+% % %   but backward compatible with KSVD definition of denoising
+% % 
+% % SMALL.solver(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3));
+% SMALL.solver(3).toolbox='SMALL';
+% SMALL.solver(3).name='SMALL_cgp';
+% SMALL.solver(3).param=sprintf('%d, %.2f', maxatoms, sqrt(Edata));
+% %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+% %   but backward compatible with KSVD definition of denoising
+% 
+% SMALL.solver(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3));
+
+% %%
+% %   Use RLS-DLA 
+% 
+% %   Initialising Dictionary structure
+% %   Setting Dictionary structure fields (toolbox, name, param, D and time)
+% %   to zero values
+% 
+% SMALL.DL(3)=SMALL_init_DL();
+% 
+% %   Defining fields needed for dictionary learning
+% 
+% SMALL.DL(3).toolbox = 'mpv2';
+% SMALL.DL(3).name = 'rlsdla';
+% 
+% %   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
+% 
+% SMALL.DL(3).param=struct(...
+%     'D', SMALL.Problem.initdict,...
+%     'K', SMALL.Problem.p,...
+%     'abs', Edata*Edata,...
+%     'lambda', 0.995,...
+%     'iternum',1);
+% 
+% %   Learn the dictionary
+% 
+% SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3));
+% 
+% %   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)
+% 
+% 
+
+%%
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+%SMALL.DL(3).D(:,225:256)=0;
+SMALL.Problem.A = SMALL.DL(3).D;
+SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+%maxatoms=32;
+SMALL.solver(3)=SMALL_init_solver;
+
+% Defining the parameters needed for denoising
+
+% SMALL.solver(3).toolbox='SPAMS';
+% SMALL.solver(3).name='mexLasso';
+% SMALL.solver(3).param=struct(...
+%     'mode', 1, ...
+%     'lambda',Edata*Edata,...
+%     'L', maxatoms); 
+% %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+% %   but backward compatible with KSVD definition of denoising
+% 
+% SMALL.solver(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3));
+
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.solver(3)=SMALL_init_solver;
+
+% Defining the parameters needed for image denoising
+
+SMALL.solver(3).toolbox='ompbox';
+SMALL.solver(3).name='omp2';
+SMALL.solver(3).param=struct(...
+    'epsilon',Edata,...
+    'maxatoms', maxatoms); 
+% SMALL.solver(3).toolbox='SPAMS';
+% SMALL.solver(3).name='mexLasso';
+% SMALL.solver(3).param=struct(...
+%     'mode', 2, ...
+%     'lambda',40,...
+%     'L', maxatoms); 
+
+%   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
+%   but backward compatible with KSVD definition of denoising
+
+SMALL.solver(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3));
+% Plot results and save midi files
+SMALL.solver(3).reconstructed.psnr
+% show results %
+
+SMALL_ImgDeNoiseResult(SMALL);
+end
+results(noise_ind,im_num).psnr.ksvd=SMALL.solver(1).reconstructed.psnr;
+results(noise_ind,im_num).psnr.odct=SMALL.solver(2).reconstructed.psnr;
+results(noise_ind,im_num).psnr.rlsdla=SMALL.solver(3).reconstructed.psnr;
+results(noise_ind,im_num).vmrse.ksvd=SMALL.solver(1).reconstructed.vmrse;
+results(noise_ind,im_num).vmrse.odct=SMALL.solver(2).reconstructed.vmrse;
+results(noise_ind,im_num).vmrse.rlsdla=SMALL.solver(3).reconstructed.vmrse;
+results(noise_ind,im_num).ssim.ksvd=SMALL.solver(1).reconstructed.ssim;
+results(noise_ind,im_num).ssim.odct=SMALL.solver(2).reconstructed.ssim;
+results(noise_ind,im_num).ssim.rlsdla=SMALL.solver(3).reconstructed.ssim;
+
+results(noise_ind,im_num).time.ksvd=SMALL.solver(1).time+SMALL.DL(1).time;
+results(noise_ind,im_num).time.rlsdla.time=SMALL.solver(3).time+SMALL.DL(3).time;
+%clear SMALL;
+end
+end
+save results.mat results