diff examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m @ 6:f72603404233

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+++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m	Mon Mar 22 10:45:01 2010 +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.
+%   We tested time and psnr for two dictionary learning techniques. This
+%   example does not represnt any extensive testing. The aim of this
+%   example is just to show how SMALL structure can be used for testing.
+%
+%   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.
+%   -   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 all;
+
+%% Load an image
+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);
+
+%   number of different values we want to test
+n =5;
+step = floor((size(test_image,1)-8+1)*(size(test_image,2)-8+1)/n);
+Training_size=zeros(1,n);
+time = zeros(2,n);
+psnr = zeros(2,n);
+for i=1:n
+    
+    %   Here we want to test time spent and quality of denoising for
+    %   different sizes of training sample.
+    Training_size(i)=i*step;
+    
+    SMALL.Problem = generateImageDenoiseProblem(test_image,Training_size(i));
+    SMALL.Problem.name=name;
+    %%
+    %   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;
+    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));
+    
+    %   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;
+    
+    
+    %%
+    %   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 denoising
+    
+    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));
+    
+    %%
+    %   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(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(...
+        'D', SMALL.Problem.initdict,...
+        'K', SMALL.Problem.p,...
+        'lambda', 2,...
+        'iter', 300,...
+        'mode', 3,...
+        'modeD', 0 );
+    
+    %   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;
+    
+    
+    %%
+    %   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 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));
+    
+    
+    
+    %% show results %%
+    %   This will show denoised images and dictionaries for all training sets.
+    %   If you are not interested to see them and do not want clutter your
+    %   screen comment following line
+    
+    SMALL_ImgDeNoiseResult(SMALL);
+    
+    time(1,i) = SMALL.DL(1).time;
+    psnr(1,i) = SMALL.solver(1).reconstructed.psnr;
+    
+    time(2,i) = SMALL.DL(2).time;
+    psnr(2,i) = SMALL.solver(2).reconstructed.psnr;
+    
+    clear SMALL
+end
+
+%% show time and psnr %%
+figure('Name', 'KSVD vs SPAMS');
+
+subplot(1,2,1); plot(Training_size, time(1,:), 'ro-', Training_size, time(2,:), 'b*-');
+legend('KSVD','SPAMS',0);
+title('Time vs Training size');
+subplot(1,2,2); plot(Training_size, psnr(1,:), 'ro-', Training_size, psnr(2,:), 'b*-');
+legend('KSVD','SPAMS',0);
+title('PSNR vs Training size');
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