comparison examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLAvsTwoStepMOD.m @ 152:485747bf39e0 ivand_dev

Two step dictonary learning - Integration of the code for dictionary update and dictionary decorrelation from Boris Mailhe

+%%  Dictionary Learning for Image Denoising - KSVD vs Recursive Least Squares
+%
+%   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.
+%   Two 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.
+%   -   RLS-DLA - Skretting, K.; Engan, K.; , "Recursive Least Squares
+%       Dictionary Learning Algorithm," Signal Processing, IEEE Transactions on,
+%       vol.58, no.4, pp.2121-2130, April 2010
+%
+
+
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2011 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.
+%   
+%%
+
+
+
+%   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.
+%   Here we are loading test_image.mat that contains structure with 5 images : lena,
+%   barbara,boat, house and peppers.
+clear;
+TMPpath=pwd;
+FS=filesep;
+[pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
+cd([pathstr1,FS,'data',FS,'images']);
+load('test_image.mat');
+cd(TMPpath);
+
+%   Deffining the noise levels that we want to test
+
+noise_level=[10 20 25 50 100];
+
+%   Here we loop through different noise levels and images 
+
+for noise_ind=2:2
+for im_num=1:1
+
+% Defining Image Denoising Problem as Dictionary Learning
+% Problem. As an input we set the number of training patches.
+
+SMALL.Problem = generateImageDenoiseProblem(test_image(im_num).i, 40000, '',256, noise_level(noise_ind));
+SMALL.Problem.name=int2str(im_num);
+
+Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
+maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
+
+%   results structure is to store all results
+
+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.
+
+
+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 - find the sparse solution in the learned
+%   dictionary for all patches in the image and the end it uses
+%   reconstruction function to reconstruct the patches and put them into a
+%   denoised image
+
+SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
+
+%   Show PSNR after reconstruction
+
+SMALL.solver(1).reconstructed.psnr
+
+%%
+%   For comparison purposes we will denoise image with overcomplete DCT
+%   here
+%   Set SMALL.Problem.A dictionary to be oDCT (i.e. Problem.initdict -
+%   since initial dictionaruy is already set to be oDCT when generating the
+%   denoising problem
+
+
+%   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); 
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time)
+%   to zero values
+
+SMALL.DL(2)=SMALL_init_DL('TwoStepDL', 'MOD', '', 1);
+
+
+%   Defining the parameters for MOD
+%   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(2).param=struct(...
+    'solver', SMALL.solver(2),...
+    'initdict', SMALL.Problem.initdict,...
+    'dictsize', SMALL.Problem.p,...
+    'iternum', 40,...
+    'mu', 0.7,...
+    'show_dict', 1);
+
+%   Learn the dictionary
+
+SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
+
+%   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(2).D;
+SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+
+%   Denoising the image - find the sparse solution in the learned
+%   dictionary for all patches in the image and the end it uses
+%   reconstruction function to reconstruct the patches and put them into a
+%   denoised image
+
+SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+
+%%
+% In the b1 field all patches from the image are stored. For RLS-DLA we
+% will first exclude all the patches that have l2 norm smaller then
+% threshold and then take min(40000, number_of_remaining_patches) in
+% ascending order as our training set (SMALL.Problem.b)
+
+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)>40000
+    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
+
+%   Forgetting factor for RLS-DLA algorithm, in this case we are using
+%   fixed value
+
+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,...
+    'show_dict', 1000);
+
+
+SMALL.DL(3) = SMALL_learn(SMALL.Problem, SMALL.DL(3));
+
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution,
+%   reconstructed and time) to zero values
+
+SMALL.Problem.A = SMALL.DL(3).D;
+SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
+
+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)=SMALL_solve(SMALL.Problem, SMALL.solver(3));
+
+SMALL.solver(3).reconstructed.psnr
+
+
+% show results %
+
+SMALL_ImgDeNoiseResult(SMALL);
+
+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