Mercurial > hg > smallbox
diff examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m @ 6:f72603404233
(none)
author | idamnjanovic |
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date | Mon, 22 Mar 2010 10:45:01 +0000 |
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children | 79e1d62f0115 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/examples/Image Denoising/SMALL_ImgDenoise_DL_test_Training_size.m Mon Mar 22 10:45:01 2010 +0000 @@ -0,0 +1,189 @@ +%% 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'); \ No newline at end of file