Mercurial > hg > smallbox
view examples/Image Denoising/SMALL_ImgDenoise_dic_ODCT_solvers_OMP_BPDN_etc_test.m @ 83:4302a91e6033
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author | Maria Jafari <maria.jafari@eecs.qmul.ac.uk> |
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date | Fri, 01 Apr 2011 12:11:16 +0100 |
parents | 984c3c175be2 |
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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. % 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');