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changeset 159:23763c5fbda5 danieleb

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Merge
author Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk>
date Wed, 31 Aug 2011 10:43:32 +0100
parents 855aa3288394 (diff) b14209313ba4 (current diff)
children e3035d45d014
files util/SMALL_solve.m
diffstat 7 files changed, 297 insertions(+), 7 deletions(-) [+]
line wrap: on
line diff
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/.hgignore	Wed Aug 31 10:43:32 2011 +0100
@@ -0,0 +1,20 @@
+toolboxes/CVX
+toolboxes/GPSR
+toolboxes/KSVD
+toolboxes/KSVDS
+toolboxes/SPARCO
+toolboxes/SparseLab
+toolboxes/Sparsify
+toolboxes/SPGL1
+solvers/SMALL_ompGabor/omp2mexGabor\.mexmaci64
+solvers/SMALL_ompGabor/ompmexGabor\.mexmaci64
+util/ksvd utils/addtocols\.mexmaci64
+util/ksvd utils/col2imstep\.mexmaci64
+util/ksvd utils/collincomb\.mexmaci64
+util/ksvd utils/im2colstep\.mexmaci64
+util/ksvd utils/rowlincomb\.mexmaci64
+util/ksvd utils/sprow\.mexmaci64
+util/Rice Wavelet Toolbox/mdwt\.mexmaci64
+util/Rice Wavelet Toolbox/midwt\.mexmaci64
+util/Rice Wavelet Toolbox/mirdwt\.mexmaci64
+util/Rice Wavelet Toolbox/mrdwt\.mexmaci64
--- a/.hgtags	Mon Aug 22 11:46:35 2011 +0100
+++ b/.hgtags	Wed Aug 31 10:43:32 2011 +0100
@@ -3,3 +3,4 @@
 9ff69e8e049f936804d0e5876cd4d367be9f3c4a backup 14032011
 b14e1f6ee4bea90a8a894a52c1114a72aa818071 ver_1.1
 19e0af5709140e163faaf9d8cf4b83a664be1edc release_1.5
+a4d0977d45956b96579a3ed83a4e6a1869ee6055 danieleb
--- a/DL/two-step DL/SMALL_two_step_DL.m	Mon Aug 22 11:46:35 2011 +0100
+++ b/DL/two-step DL/SMALL_two_step_DL.m	Wed Aug 31 10:43:32 2011 +0100
@@ -76,7 +76,7 @@
 end
 % determine if we should do decorrelation in every iteration  %
 
-if isfield(DL.param,'coherence')
+if isfield(DL.param,'coherence') && isscalar(DL.param.coherence)
     decorrelate = 1;
     mu = DL.param.coherence;
 else
@@ -108,13 +108,20 @@
 % main loop %
 
 for i = 1:iternum
+    disp([num2str(i) '/' num2str(iternum)]);
     Problem.A = dico;
     solver = SMALL_solve(Problem, solver);
     [dico, solver.solution] = dico_update(dico, sig, solver.solution, ...
         typeUpdate, flow, learningRate);
-    if (decorrelate)
-        dico = dico_decorr(dico, mu, solver.solution);
-    end
+    dico = normcols(dico);
+        switch DL.param.decFcn
+            case 'mailhe'
+                dico = dico_decorr(dico, mu, solver.solution);
+            case 'tropp'
+                [n m] = size(dico);
+                dico = grassmanian(n,m,[],[],[],dico,true);
+            otherwise
+        end
     
    if ((show_dictionary)&&(mod(i,show_iter)==0))
        dictimg = SMALL_showdict(dico,[8 8],...
@@ -139,4 +146,4 @@
   Y(blockids) = sum(X(:,blockids).^2);
 end
 
-end
\ No newline at end of file
+end
--- a/DL/two-step DL/dico_decorr.m	Mon Aug 22 11:46:35 2011 +0100
+++ b/DL/two-step DL/dico_decorr.m	Wed Aug 31 10:43:32 2011 +0100
@@ -9,6 +9,8 @@
     %   Result:
     %   dico: a dictionary close to the input one with coherence mu.
     
+    eps = 1e-6; % define tolerance for normalisation term alpha
+    
     % compute atom weights
     if nargin > 2
         rank = sum(amp.*amp, 2);
@@ -20,7 +22,7 @@
     % coherence mu. niter can be adjusted to needs.
     niter = 1;
     while niter < 5 && ...
-            max(max(abs(dico'*dico -eye(length(dico))))) > mu + 10^-6
+            max(max(abs(dico'*dico -eye(length(dico))))) > mu + eps
         % find pairs of high correlation atoms
         colors = dico_color(dico, mu);
         
@@ -36,7 +38,7 @@
                 
                 % update the atom
                 corr = dico(:,index(1))'*dico(:,index(2));
-                alpha = sqrt((1-mu*mu)/(1-corr*corr));
+                alpha = sqrt((1-mu*mu)/(1-corr^2+eps));
                 beta = corr*alpha-mu*sign(corr);
                 dico(:,index(2)) = alpha*dico(:,index(2))...
                     -beta*dico(:,index(1));
Binary file data/audio/wav/oboe.mf.c4b4.wav has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/SMALL_test_coherence.m	Wed Aug 31 10:43:32 2011 +0100
@@ -0,0 +1,210 @@
+clear
+
+%% Parameteres
+
+% Dictionary learning parameters
+toolbox   = 'TwoStepDL'; %dictionary learning toolbox
+dicUpdate = {'ksvd','mailhe'}; %dictionary updates
+iterNum   = 20; %number of iterations
+
+% Test signal parameters
+signal    = audio('music03_16kHz.wav'); %audio signal
+blockSize = 256; %size of audio frames
+dictSize  = 512; %number of atoms in the dictionary
+overlap   = 0.5; %overlap between consecutive frames
+sigma     = 1e6; %snr of noise (set to be negligible so that the problem becomes approximation rather than denoising)
+percActiveAtoms = 5; %percentage of active atoms
+
+% Dependent parameters
+nActiveAtoms = fix(blockSize/100*percActiveAtoms); %number of active atoms
+epsilon      = 1/sigma; %error constraint for sparse representation step (corresponds to noise applied to signals)
+minCoherence = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1))); %target coherence (based on coherence lower bound)
+
+% Initial dictionaries
+dctDict = dictionary('dct',blockSize,dictSize);
+dctDict = dctDict.phi;
+gaborParam = struct('N',blockSize,'redundancyFactor',2,'wd',@rectwin);
+gaborDict = Gabor_Dictionary(gaborParam);
+
+%% Generate audio denoising problem with low noise (audio representation)
+SMALL.Problem = generateAudioDenoiseProblem(signal.s,[],blockSize,...
+    dictSize,overlap,sigma); % generate representation problem
+SMALL.Problem.b1 = SMALL.Problem.b; % copy signals from training set b to test set b1 (needed for later functions)
+
+% omp2 sparse representation solver
+ompParam = struct('X',SMALL.Problem.b,'epsilon',epsilon,'maxatoms',nActiveAtoms); %parameters
+solver = SMALL_init_solver('ompbox','omp2',ompParam,false); %solver structure
+
+
+%% Test ksvd dictionary update
+name = dicUpdate{1}; %use ksvd update
+SMALL.DL(1:9) = SMALL_init_DL(toolbox,name); %create dictionary learning structures
+
+% learn with random initialisation and no decorrelation
+SMALL.DL(1).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','none'); %parameters for the dictionary learning
+SMALL.DL(1) = SMALL_learn(SMALL.Problem,SMALL.DL(1)); %learn dictionary
+
+% learn with random initialisation and mailhe decorrelation
+SMALL.DL(2).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','mailhe','coherence',minCoherence); %parameters for the dictionary learning
+SMALL.DL(2) = SMALL_learn(SMALL.Problem,SMALL.DL(2)); %learn dictionary
+
+% learn with random initialisation and tropp decorrelation
+SMALL.DL(3).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','tropp','coherence',minCoherence); %parameters for the dictionary learning
+SMALL.DL(3) = SMALL_learn(SMALL.Problem,SMALL.DL(3)); %learn dictionary
+
+% Learn with dct initialisation and no decorrelation
+SMALL.DL(4).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','none','initdict',dctDict); %parameters for the dictionary learning
+SMALL.DL(4) = SMALL_learn(SMALL.Problem,SMALL.DL(4)); %learn dictionary
+
+% learn with dct initialisation and mailhe decorrelation
+SMALL.DL(5).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','mailhe','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning
+SMALL.DL(5) = SMALL_learn(SMALL.Problem,SMALL.DL(5)); %learn dictionary
+
+% learn with dct initialisation and tropp decorrelation
+SMALL.DL(6).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','tropp','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning
+SMALL.DL(6) = SMALL_learn(SMALL.Problem,SMALL.DL(6)); %learn dictionary
+
+% Learn with gabor initialisation and no decorrelation
+SMALL.DL(7).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','none','initdict',gaborDict); %parameters for the dictionary learning
+SMALL.DL(7) = SMALL_learn(SMALL.Problem,SMALL.DL(7)); %learn dictionary
+
+% learn with gabor initialisation and mailhe decorrelation
+SMALL.DL(8).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','mailhe','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning
+SMALL.DL(8) = SMALL_learn(SMALL.Problem,SMALL.DL(8)); %learn dictionary
+
+% learn with gabor initialisation and tropp decorrelation
+SMALL.DL(9).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','tropp','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning
+SMALL.DL(9) = SMALL_learn(SMALL.Problem,SMALL.DL(9)); %learn dictionary
+
+%% Test mailhe dictionary update
+name = dicUpdate{2}; %use mailhe update
+SMALL.DL(10:18) = SMALL_init_DL(toolbox,name); %create dictionary learning structure
+
+% learn with random initialisation and no decorrelation
+SMALL.DL(10).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','none'); %parameters for the dictionary learning
+SMALL.DL(10) = SMALL_learn(SMALL.Problem,SMALL.DL(10)); %learn dictionary
+
+% learn with random initialisation and mailhe decorrelation
+SMALL.DL(11).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','mailhe','coherence',minCoherence); %parameters for the dictionary learning
+SMALL.DL(11) = SMALL_learn(SMALL.Problem,SMALL.DL(11)); %learn dictionary
+
+% learn with random initialisation and tropp decorrelation
+SMALL.DL(12).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','tropp','coherence',minCoherence); %parameters for the dictionary learning
+SMALL.DL(12) = SMALL_learn(SMALL.Problem,SMALL.DL(12)); %learn dictionary
+
+% Learn with dct initialisation and no decorrelation
+SMALL.DL(13).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','none','initdict',dctDict); %parameters for the dictionary learning
+SMALL.DL(13) = SMALL_learn(SMALL.Problem,SMALL.DL(13)); %learn dictionary
+
+% learn with dct initialisation and mailhe decorrelation
+SMALL.DL(14).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','mailhe','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning
+SMALL.DL(14) = SMALL_learn(SMALL.Problem,SMALL.DL(14)); %learn dictionary
+
+% learn with dct initialisation and tropp decorrelation
+SMALL.DL(15).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','tropp','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning
+SMALL.DL(15) = SMALL_learn(SMALL.Problem,SMALL.DL(15)); %learn dictionary
+
+% Learn with gabor initialisation and no decorrelation
+SMALL.DL(16).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','none','initdict',gaborDict); %parameters for the dictionary learning
+SMALL.DL(16) = SMALL_learn(SMALL.Problem,SMALL.DL(16)); %learn dictionary
+
+% learn with gabor initialisation and mailhe decorrelation
+SMALL.DL(17).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','mailhe','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning
+SMALL.DL(17) = SMALL_learn(SMALL.Problem,SMALL.DL(17)); %learn dictionary
+
+% learn with gabor initialisation and tropp decorrelation
+SMALL.DL(18).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,...
+    'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,...
+    'decFcn','tropp','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning
+SMALL.DL(18) = SMALL_learn(SMALL.Problem,SMALL.DL(18)); %learn dictionary
+
+%% Evaluate coherence and snr of representation for the various methods
+sigNoiseRatio = zeros(18,1);
+mu = zeros(18,1);
+for i=1:18
+    SMALL.Problem.A = SMALL.DL(i).D;
+    tempSolver = SMALL_solve(SMALL.Problem,solver);
+    sigNoiseRatio(i) = snr(SMALL.Problem.b,SMALL.DL(i).D*tempSolver.solution);
+    dic(i) = dictionary(SMALL.DL(i).D);
+    mu(i) = dic(i).coherence;
+end
+
+%% Plot results
+minMu = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1)));
+maxSNR = max(sigNoiseRatio);
+
+figure, subplot(2,2,1)
+snrMat = buffer(sigNoiseRatio(1:9),3);
+bar(snrMat');
+title('SNR - KSVD Update')
+xlabel('Initial dictionary')
+ylabel('SNR (dB)')
+set(gca,'XTickLabel',{'data','dct','gabor'},'YLim',[0 maxSNR+1]);
+legend('none','Mailhe','Tropp')
+grid on
+
+subplot(2,2,2), grid on
+snrMat = buffer(sigNoiseRatio(10:18),3);
+bar(snrMat');
+title('SNR - Mailhe Update')
+xlabel('Initial dictionary')
+ylabel('SNR (dB)')
+set(gca,'XTickLabel',{'data','dct','gabor'},'YLim',[0 maxSNR+1]);
+legend('none','Mailhe','Tropp')
+grid on
+
+subplot(2,2,3), hold on, grid on
+title('Coherence - KSVD Update')
+muMat = buffer(mu(1:9),3);
+line([0.5 3.5],[1 1],'Color','r');
+bar(muMat');
+line([0.5 3.5],[minMu minMu],'Color','k');
+set(gca,'XTick',1:3,'XTickLabel',{'data','dct','gabor'},'YLim',[0 1.05])
+legend('\mu_{max}','none','Mailhe','Tropp','\mu_{min}')
+ylabel('\mu')
+xlabel('Initial dictionary')
+
+subplot(2,2,4), hold on, grid on
+title('Coherence - Mailhe Update')
+muMat = buffer(mu(10:18),3);
+line([0.5 3.5],[1 1],'Color','r');
+bar(muMat');
+line([0.5 3.5],[minMu minMu],'Color','k');
+set(gca,'XTick',1:3,'XTickLabel',{'data','dct','gabor'},'YLim',[0 1.05])
+legend('\mu_{max}','none','Mailhe','Tropp','\mu_{min}')
+ylabel('\mu')
+xlabel('Initial dictionary')
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/util/SMALL_ImgDeNoiseResult.m.orig	Wed Aug 31 10:43:32 2011 +0100
@@ -0,0 +1,50 @@
+function SMALL_ImgDeNoiseResult(SMALL)   
+%%  Represents the results of Dictionary Learning for Image denoising
+%
+%   Function gets as input SMALL structure and plots  Image Denoise
+%   results: Original Image, Noisy Image and for learned dictionaries and 
+%   denoised images 
+%
+
+%   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.
+%%
+
+
+figure('Name', sprintf('Image %s (training set size- %d, sigma - %d)',SMALL.Problem.name, SMALL.Problem.n, SMALL.Problem.sigma));
+
+m=size(SMALL.solver,2)+1;
+maxval=SMALL.Problem.maxval;
+im=SMALL.Problem.Original;
+imnoise=SMALL.Problem.Noisy;
+
+subplot(2, m, 1); imagesc(im/maxval);colormap(gray);axis off; axis image;        % Set aspect ratio to obtain square pixels
+title('Original image');
+
+subplot(2,m,m+1); imagesc(imnoise/maxval);axis off; axis image; 
+title(sprintf('Noisy image, PSNR = %.2fdB', SMALL.Problem.noisy_psnr ));
+
+for i=2:m
+    
+    subplot(2, m, i); imagesc(SMALL.solver(i-1).reconstructed.Image/maxval);axis off; axis image; 
+    title(sprintf('%s Denoised image, PSNR: %.2f dB in %.2f s',...
+        SMALL.DL(i-1).name, SMALL.solver(i-1).reconstructed.psnr, SMALL.solver(i-1).time ),'Interpreter','none');
+    if strcmpi(SMALL.DL(i-1).name,'ksvds')
+        D = kron(SMALL.Problem.basedict{2},SMALL.Problem.basedict{1})*SMALL.DL(i-1).D;
+    else
+        D = SMALL.DL(i-1).D;
+    end
+    dictimg = SMALL_showdict(D,SMALL.Problem.blocksize,...
+        round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
+    
+    subplot(2,m,m+i);imagesc(dictimg);axis off; axis image; 
+    title(sprintf('%s dictionary in %.2f s',...
+        SMALL.DL(i-1).name, SMALL.DL(i-1).time),'Interpreter','none');
+    
+end