# HG changeset patch # User idamnjanovic # Date 1300117319 0 # Node ID 984c3c175be283825dbc424c9667a352a2a54404 # Parent 623fcf3a69b1cfd0c8b2bd075881c4ed98664e48 diff -r 623fcf3a69b1 -r 984c3c175be2 examples/Image Denoising/SMALL_ImgDenoise_dic_ODCT_solvers_OMP_BPDN_etc_test.m --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/examples/Image Denoising/SMALL_ImgDenoise_dic_ODCT_solvers_OMP_BPDN_etc_test.m Mon Mar 14 15:41:59 2011 +0000 @@ -0,0 +1,163 @@ +%% 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. +% Three 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. +% - KSVDS - R. Rubinstein, M. Zibulevsky, and M. Elad, "Learning Sparse +% Dictionaries for Sparse Signal Approximation", Technical +% Report - CS, Technion, June 2009. +% - 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; + +% 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. + +% 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); +% SMALL.Problem.name=name; + + +% Defining Image Denoising Problem as Dictionary Learning +% Problem. As an input we set the number of training patches. + +SMALL.Problem = generateImageDenoiseProblem('', 40000, '','', 20); + +Edata=sqrt(prod(SMALL.Problem.blocksize)) * SMALL.Problem.sigma * SMALL.Problem.gain; +maxatoms = floor(prod(SMALL.Problem.blocksize)/2); +%% 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,... +% 'iternum', 20,... +% 'memusage', 'high'); +% +% % Learn the dictionary +% +% SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1)); +%% Initialising Dictionary structure +% Setting Dictionary structure fields (toolbox, name, param, D and time) +% to zero values +% + +SMALL.DL(1)=SMALL_init_DL(); +% Take initial dictonary (overcomplete DCT) to be a final dictionary for +% reconstruction + +SMALL.DL(1).D=SMALL.Problem.initdict; +%% + +% 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; + +SparseDict=0; +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 + + +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 - 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. + +SMALL.solver(1)=SMALL_solve(SMALL.Problem, SMALL.solver(1)); + +%% +% Initialising solver structure +% Setting solver structure fields (toolbox, name, param, solution, +% reconstructed and time) to zero values +lam=2*SMALL.Problem.sigma;%*sqrt(2*log2(size(SMALL.Problem.A,1))) +for i=1:11 + lambda(i)=lam+5-(i-1); +SMALL.DL(2)=SMALL_init_DL(); +i +%SMALL.Problem.A = SMALL.Problem.initdict; +SMALL.DL(2).D=SMALL.Problem.initdict; +SMALL.solver(2)=SMALL_init_solver; + +% Defining the parameters needed for image denoising + +SMALL.solver(2).toolbox='SPAMS'; +SMALL.solver(2).name='mexLasso'; +SMALL.solver(2).param=struct(... + 'mode', 2, ... + 'lambda',lambda(i),... + 'L', 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. + +SMALL.solver(2)=SMALL_solve(SMALL.Problem, SMALL.solver(2)); + + +% show results % + +%SMALL_ImgDeNoiseResult(SMALL); + + time(1,i) = SMALL.solver(2).time; + psnr(1,i) = SMALL.solver(2).reconstructed.psnr; +end%% show time and psnr %% +figure('Name', 'SPAMS LAMBDA TEST'); + +subplot(1,2,1); plot(lambda, time(1,:), 'ro-'); +title('time vs lambda'); +subplot(1,2,2); plot(lambda, psnr(1,:), 'b*-'); +title('PSNR vs lambda'); \ No newline at end of file