changeset 107:dab78a3598b6

changes to comments for couple of scripts
author Ivan Damnjanovic lnx <ivan.damnjanovic@eecs.qmul.ac.uk>
date Wed, 18 May 2011 11:50:12 +0100 (2011-05-18)
parents fd1c32cda22c
children b14e1f6ee4be
files SMALLboxSetup.m examples/Automatic Music Transcription/SMALL_AMT_DL_test.m examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m examples/Pierre Villars/Pierre_Villars_Example.m util/AMT_analysis.m
diffstat 9 files changed, 310 insertions(+), 247 deletions(-) [+]
line wrap: on
line diff
--- a/SMALLboxSetup.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/SMALLboxSetup.m	Wed May 18 11:50:12 2011 +0100
@@ -1,5 +1,13 @@
 function SMALLboxSetup(varargin)
 %%% SMALLboxSetup 
+% 
+%   Will automatically download and install existing toolboxes
+%   on sparse representations and dictionary learning
+% 
+%   For this function an internet connection is required.  
+%
+%   SMALLbox initialisation
+
 %
 %   Centre for Digital Music, Queen Mary, University of London.
 %   This file copyright 2009 Ivan Damnjanovic, Matthew Davies.
@@ -10,13 +18,6 @@
 %   License, or (at your option) any later version.  See the file
 %   COPYING included with this distribution for more information.
 %   
-% 
-%   Will automatically download and install existing toolboxes
-%   on sparse representations and dictionary learning
-% 
-%   For this function an internet connection is required.  
-%
-%   SMALLbox initialisation
 %%
 clc;
 
--- a/examples/Automatic Music Transcription/SMALL_AMT_DL_test.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/examples/Automatic Music Transcription/SMALL_AMT_DL_test.m	Wed May 18 11:50:12 2011 +0100
@@ -1,13 +1,4 @@
 %%  DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1
-%
-%   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.
 %   
 %   This file contains an example of how SMALLbox can be used to test diferent
 %   dictionary learning techniques in Automatic Music Transcription problem.
@@ -18,7 +9,16 @@
 %   The function will generarte the Problem structure that is used to learn
 %   Problem.p notes spectrograms from training set Problem.b using
 %   dictionary learning technique defined in DL structure.
+
 %
+%   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.
 %%
 
 clear;
@@ -45,13 +45,13 @@
 SMALL.DL(1).name = 'ksvd';
 %   Defining the parameters for KSVD
 %   In this example we are learning 88 atoms in 100 iterations, so that
-%   every frame in the training set can be represented with maximum 3
+%   every frame in the training set can be represented with maximum Tdata
 %   dictionary elements. Type help ksvd in MATLAB prompt for more options.
 
 SMALL.DL(1).param=struct(...
-    'Tdata', 10,...
+    'Tdata', 3,...
     'dictsize', SMALL.Problem.p,...
-    'iternum', 100);
+    'iternum', 50);
 
 % Learn the dictionary
 
@@ -110,82 +110,82 @@
 
 %%
 
-% %   Here we solve the same problem using non-negative sparse coding with 
-% %   SPAMS online dictionary learning (Julien Mairal 2009)
-% %
-% 
-% %   Initialising Dictionary structure
-% %   Setting Dictionary structure fields (toolbox, name, param, D and time) 
-% %   to zero values
-% 
-% SMALL.DL(2)=SMALL_init_DL();
-% 
-% 
-% %   Defining fields needed for dictionary learning
-% 
-% SMALL.DL(2).toolbox = 'SPAMS';
-% SMALL.DL(2).name = 'mexTrainDL';
-% 
-% %   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
-% 
-% SMALL.DL(2).param=struct(...
-%     'K', SMALL.Problem.p,...
-%     'lambda', 3,...
-%     'iter', 300,...
-%     'posAlpha', 1,...
-%     'posD', 1,...
-%     'whiten', 0,...
-%     'mode', 2);
-% 
-% %   Learn the dictionary
-% 
-% SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
-% 
-% %   Set SMALL.Problem.A dictionary and reconstruction function 
-% %   (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) SMALL_midiGenerate(x, SMALL.Problem);
-% 
-% %%
-% %   Initialising solver structure
-% %   Setting solver structure fields (toolbox, name, param, solution, 
-% %   reconstructed and time) to zero values
-% %   As an example, SPAMS (Julien Mairal 2009) implementation of LARS
-% %   algorithm is used for representation of training set in the learned
-% %   dictionary.
-% 
-% SMALL.solver(2)=SMALL_init_solver;
-% 
-% %   Defining the parameters needed for sparse representation
-% 
-% SMALL.solver(2).toolbox='SPAMS';
-% SMALL.solver(2).name='mexLasso';
-% 
-% %   Here we use mexLasso mode=2, with lambda=3, lambda2=0 and positivity
-% %   constrain (type 'help mexLasso' for more information about modes):
-% %   
-% %   min_{alpha_i} (1/2)||x_i-Dalpha_i||_2^2 + lambda||alpha_i||_1 + (1/2)lambda2||alpha_i||_2^2
-% 
-% SMALL.solver(2).param=struct('lambda', 3, 'pos', 1, 'mode', 2);
-% 
-% %   Call SMALL_soolve to represent the signal in the given dictionary. 
-% %   As a final command SMALL_solve will call above defined reconstruction
-% %   function to reconstruct the training set (Problem.b) in the learned 
-% %   dictionary (Problem.A)
-% 
-% SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
-% 
-% %%
-% %   Analysis of the result of automatic music transcription. If groundtruth
-% %   exists, we can compare transcribed notes and original and get usual
-% %   True Positives, False Positives and False Negatives measures.
-% 
-% if ~isempty(SMALL.Problem.notesOriginal)
-%     AMT_res(2) = AMT_analysis(SMALL.Problem, SMALL.solver(2));
-% end
+%   Here we solve the same problem using non-negative sparse coding with 
+%   SPAMS online dictionary learning (Julien Mairal 2009)
+%
+
+%   Initialising Dictionary structure
+%   Setting Dictionary structure fields (toolbox, name, param, D and time) 
+%   to zero values
+
+SMALL.DL(2)=SMALL_init_DL();
+
+
+%   Defining fields needed for dictionary learning
+
+SMALL.DL(2).toolbox = 'SPAMS';
+SMALL.DL(2).name = 'mexTrainDL';
+
+%   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
+
+SMALL.DL(2).param=struct(...
+    'K', SMALL.Problem.p,...
+    'lambda', 3,...
+    'iter', 300,...
+    'posAlpha', 1,...
+    'posD', 1,...
+    'whiten', 0,...
+    'mode', 2);
+
+%   Learn the dictionary
+
+SMALL.DL(2) = SMALL_learn(SMALL.Problem, SMALL.DL(2));
+
+%   Set SMALL.Problem.A dictionary and reconstruction function 
+%   (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) SMALL_midiGenerate(x, SMALL.Problem);
+
+%%
+%   Initialising solver structure
+%   Setting solver structure fields (toolbox, name, param, solution, 
+%   reconstructed and time) to zero values
+%   As an example, SPAMS (Julien Mairal 2009) implementation of LARS
+%   algorithm is used for representation of training set in the learned
+%   dictionary.
+
+SMALL.solver(2)=SMALL_init_solver;
+
+%   Defining the parameters needed for sparse representation
+
+SMALL.solver(2).toolbox='SPAMS';
+SMALL.solver(2).name='mexLasso';
+
+%   Here we use mexLasso mode=2, with lambda=3, lambda2=0 and positivity
+%   constrain (type 'help mexLasso' for more information about modes):
+%   
+%   min_{alpha_i} (1/2)||x_i-Dalpha_i||_2^2 + lambda||alpha_i||_1 + (1/2)lambda2||alpha_i||_2^2
+
+SMALL.solver(2).param=struct('lambda', 3, 'pos', 1, 'mode', 2);
+
+%   Call SMALL_soolve to represent the signal in the given dictionary. 
+%   As a final command SMALL_solve will call above defined reconstruction
+%   function to reconstruct the training set (Problem.b) in the learned 
+%   dictionary (Problem.A)
+
+SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+
+%%
+%   Analysis of the result of automatic music transcription. If groundtruth
+%   exists, we can compare transcribed notes and original and get usual
+%   True Positives, False Positives and False Negatives measures.
+
+if ~isempty(SMALL.Problem.notesOriginal)
+    AMT_res(2) = AMT_analysis(SMALL.Problem, SMALL.solver(2));
+end
 
 %%
 % Plot results and save midi files
--- a/examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/examples/Automatic Music Transcription/SMALL_AMT_SPAMS_test.m	Wed May 18 11:50:12 2011 +0100
@@ -1,13 +1,4 @@
 %%  DICTIONARY LEARNING FOR AUTOMATIC MUSIC TRANSCRIPTION EXAMPLE 1
-%
-%   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.
 %   
 %   This file contains an example of how SMALLbox can be used to test diferent
 %   dictionary learning techniques in Automatic Music Transcription problem.
@@ -18,6 +9,16 @@
 %   The function will generarte the Problem structure that is used to learn
 %   Problem.p notes spectrograms from training set Problem.b using
 %   dictionary learning technique defined in DL structure.
+
+%
+%   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.
 %
 %%
 
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsRLSDLA.m	Wed May 18 11:50:12 2011 +0100
@@ -250,7 +250,7 @@
 
 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;
+clear SMALL;
 end
 end
-save results.mat results
+% save results.mat results
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_KSVDvsSPAMS.m	Wed May 18 11:50:12 2011 +0100
@@ -1,13 +1,5 @@
 %% DICTIONARY LEARNING FOR IMAGE DENOISING
-%
-%   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2009 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.
+
 %   
 %   This file contains an example of how SMALLbox can be used to test different
 %   dictionary learning techniques in Image Denoising problem.
@@ -25,6 +17,16 @@
 %               Dictionary Learning for Sparse Coding. International
 %               Conference on Machine Learning,Montreal, Canada, 2009
 %
+
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 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.
 %
 %%
 
@@ -73,6 +75,8 @@
 %   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,...
@@ -90,7 +94,7 @@
 %   solver structures)
 
 SMALL.Problem.A = SMALL.DL(1).D;
-
+SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
 
 %%
 %   Initialising solver structure
@@ -102,12 +106,21 @@
 % Defining the parameters needed for image denoising
 
 SMALL.solver(1).toolbox='ompbox';
-SMALL.solver(1).name='ompdenoise';
+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
+%   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_denoise(SMALL.Problem, SMALL.solver(1));
+SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1));
+
+%   Show PSNR after reconstruction
+
+SMALL.solver(1).reconstructed.psnr
 
 %%
 % Use KSVDS Dictionary Learning Algorithm to denoise image
@@ -156,7 +169,11 @@
 SMALL.Problem.basedict{1} = SMALL.DL(2).param.basedict{1};
 SMALL.Problem.basedict{2} = SMALL.DL(2).param.basedict{2};
 
-%%
+%   Setting up reconstruction function
+
+SparseDict=1;
+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
@@ -166,69 +183,79 @@
 % Defining the parameters needed for image denoising
 
 SMALL.solver(2).toolbox='ompsbox';
-SMALL.solver(2).name='ompsdenoise';
+SMALL.solver(2).name='omps2';
+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.
+%   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_denoise(SMALL.Problem, SMALL.solver(2));
+SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
 
-% %%
-% %   Use SPAMS Online Dictionary Learning Algorithm
-% %   to Learn overcomplete dictionary (Julien Mairal 2009)
-% %   (If you have not installed SPAMS please comment the following two cells)
-% 
-% %   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 = 'SPAMS';
-% SMALL.DL(3).name = 'mexTrainDL';
-% 
-% %   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));
-% 
-% %   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(3).D;
-% 
-% 
-% %%
-% %   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 denoising
-% 
-% SMALL.solver(3).toolbox='ompbox';
-% SMALL.solver(3).name='ompdenoise';
-% 
-% %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
-% %   but backward compatible with KSVD definition of denoising
-% 
-% SMALL.solver(3)=SMALL_denoise(SMALL.Problem, SMALL.solver(3));
+%%
+%   Use SPAMS Online Dictionary Learning Algorithm
+%   to Learn overcomplete dictionary (Julien Mairal 2009)
+%   (If you have not installed SPAMS please comment the following two cells)
+
+%   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 = 'SPAMS';
+SMALL.DL(3).name = 'mexTrainDL';
+
+%   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));
+
+%   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(3).D;
+
+%   Setting up reconstruction function
+
+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(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); 
+
+%   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(3)=SMALL_solve(SMALL.Problem, SMALL.solver(3));
 
 %%
 % Plot results and save midi files
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m	Wed May 18 11:50:12 2011 +0100
@@ -1,5 +1,14 @@
 %% 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.
+%   This example can be used to test SPAMS for different values of
+%   parameter lambda. In no way it represents extensive testing of image
+%   denoising. It should only give an idea how SMALL structure can be used
+%   for testing.
+
+%
 %   Centre for Digital Music, Queen Mary, University of London.
 %   This file copyright 2010 Ivan Damnjanovic.
 %
@@ -8,14 +17,6 @@
 %   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.
-%   
-%   This file contains an example of how SMALLbox can be used to test different
-%   dictionary learning techniques in Image Denoising problem.
-%   This example can be used to test SPAMS for different values of
-%   parameter lambda. In no way it represents extensive testing of image
-%   denoising. It should only give an idea how SMALL structure can be used
-%   for testing.
-%
 %%
 
 clear all;
@@ -86,25 +87,31 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(1).D;
-    
-    
-    %%
-    %   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 sparse representation
-    
-    SMALL.solver(1).toolbox='ompbox';
-    SMALL.solver(1).name='ompdenoise';
-    
-    %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
-    %   but backward compatible with KSVD definition of denoising
-    
-    SMALL.solver(1)=SMALL_denoise(SMALL.Problem, SMALL.solver(1));
-    
+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
+Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain;
+maxatoms = floor(prod(SMALL.Problem.blocksize)/2);
+
+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 results %%
     %   This will show denoised image and dictionary for all lambdas. If you
--- a/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m	Wed May 18 11:50:12 2011 +0100
@@ -1,14 +1,6 @@
 %% DICTIONARY LEARNING FOR IMAGE DENOISING
 %
-%   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 20100 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.
-%   
+   
 %   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,
@@ -25,8 +17,16 @@
 %   -   SPAMS - J. Mairal, F. Bach, J. Ponce and G. Sapiro. Online
 %               Dictionary Learning for Sparse Coding. International
 %               Conference on Machine Learning,Montreal, Canada, 2009
+
 %
+%   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.%%
 %%
 
 clear all;
@@ -77,6 +77,8 @@
     %   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,...
@@ -94,7 +96,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(1).D;
-    
+    SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
     
     %%
     %   Initialising solver structure
@@ -107,12 +109,23 @@
     % Defining the parameters needed for denoising
     
     SMALL.solver(1).toolbox='ompbox';
-    SMALL.solver(1).name='ompdenoise';
+    SMALL.solver(1).name='omp2';
     
-    %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
-    %   but backward compatible with KSVD definition of denoising
-    
-    SMALL.solver(1)=SMALL_denoise(SMALL.Problem, SMALL.solver(1));
+    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
+
     
     %%
     %   Use SPAMS Online Dictionary Learning Algorithm
@@ -150,7 +163,7 @@
     %   solver structures)
     
     SMALL.Problem.A = SMALL.DL(2).D;
-    
+    SMALL.Problem.reconstruct = @(x) ImgDenoise_reconstruct(x, SMALL.Problem);
     
     %%
     %   Initialising solver structure
@@ -162,12 +175,18 @@
     % Defining the parameters needed for denoising
     
     SMALL.solver(2).toolbox='ompbox';
-    SMALL.solver(2).name='ompdenoise';
-    
-    %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
-    %   but backward compatible with KSVD definition of denoising
-    
-    SMALL.solver(2)=SMALL_denoise(SMALL.Problem, SMALL.solver(2));
+    SMALL.solver(2).name='omp2';
+    SMALL.solver(2).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(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2));
+ 
     
     
     
--- a/examples/Pierre Villars/Pierre_Villars_Example.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/examples/Pierre Villars/Pierre_Villars_Example.m	Wed May 18 11:50:12 2011 +0100
@@ -1,4 +1,12 @@
 %%  Pierre Villars Example
+%   
+%   This example is based on the experiment suggested by Professor Pierre
+%   Vandergheynst on the SMALL meeting in Villars.
+%   The idea behind is to use patches from source image as a dictionary in
+%   which we represent target image using matching pursuit algorithm.
+%   Calling Pierre_Problem function to get src image to be used as dictionary
+%   and target image to be represented using MP with 3 patches from source image
+
 %
 %   Centre for Digital Music, Queen Mary, University of London.
 %   This file copyright 2010 Ivan Damnjanovic.
@@ -8,13 +16,6 @@
 %   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.
-%   
-%   This example is based on the experiment suggested by Professor Pierre
-%   Vandergheynst on the SMALL meeting in Villars.
-%   The idea behind is to use patches from source image as a dictionary in
-%   which we represent target image using matching pursuit algorithm.
-%   Calling Pierre_Problem function to get src image to be used as dictionary
-%   and target image to be represented using MP with 3 patches from source image
 %
 %%
 
--- a/util/AMT_analysis.m	Tue Apr 05 17:03:26 2011 +0100
+++ b/util/AMT_analysis.m	Wed May 18 11:50:12 2011 +0100
@@ -1,14 +1,5 @@
 function AMT_res = AMT_analysis(Problem, solver)
 %%% Automatic Music Transcription results analysis
-%
-%   Centre for Digital Music, Queen Mary, University of London.
-%   This file copyright 2009 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 wav file that is transcribed is generated from midi file (i.e. if
 %   groundtruth exists) transcription is comapred to the original notes and
@@ -22,6 +13,17 @@
 %   -   FN - number of false negatives
 %   -   FP - number of false positives
 
+%
+%   Centre for Digital Music, Queen Mary, University of London.
+%   This file copyright 2009 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.
+%%
+
 timeOr=Problem.notesOriginal(:,5);
 noteOr=Problem.notesOriginal(:,3);
 timeTr=solver.reconstructed.notes(:,5);
@@ -48,7 +50,12 @@
 end
 
 AMT_res.tp_notes = [Problem.notesOriginal(Hits(:,1),[3 5]) solver.reconstructed.notes(Hits(:,2),[3 5])];
-AMT_res.oe_notes = [Problem.notesOriginal(OE(:,1),[3 5]) solver.reconstructed.notes(OE(:,2),[3 5])];
+if ~isempty(OE)
+    AMT_res.oe_notes = [Problem.notesOriginal(OE(:,1),[3 5]) solver.reconstructed.notes(OE(:,2),[3 5])];
+end
+if isempty(OE)
+    OE=[0 0];
+end
 AMT_res.fn_notes_wo_oe = Problem.notesOriginal(setdiff([1:n],union(Hits(:,1),OE(:,1))),[3 5]);
 AMT_res.fp_notes_wo_oe = solver.reconstructed.notes(setdiff([1:m],union(Hits(:,2),OE(:,2))),[3 5]);
 AMT_res.TP=size(Hits,1);