idamnjanovic@6: %% DICTIONARY LEARNING FOR IMAGE DENOISING
idamnjanovic@6: %   This file contains an example of how SMALLbox can be used to test different
idamnjanovic@6: %   dictionary learning techniques in Image Denoising problem.
idamnjanovic@6: %   This example can be used to test SPAMS for different values of
idamnjanovic@6: %   parameter lambda. In no way it represents extensive testing of image
idamnjanovic@6: %   denoising. It should only give an idea how SMALL structure can be used
idamnjanovic@6: %   for testing.
idamnjanovic@6: %
idamnjanovic@6: %   Ivan Damnjanovic 2010
idamnjanovic@6: %%
idamnjanovic@6: 
idamnjanovic@6: clear all;
idamnjanovic@6: 
idamnjanovic@6: %% Load an image
idamnjanovic@6: TMPpath=pwd;
idamnjanovic@6: FS=filesep;
idamnjanovic@6: [pathstr1, name, ext, versn] = fileparts(which('SMALLboxSetup.m'));
idamnjanovic@6: cd([pathstr1,FS,'data',FS,'images']);
idamnjanovic@6: [filename,pathname] = uigetfile({'*.png;'},'Select a file containin pre-calculated notes');
idamnjanovic@6: [pathstr, name, ext, versn] = fileparts(filename);
idamnjanovic@6: test_image = imread(filename);
idamnjanovic@6: test_image = double(test_image);
idamnjanovic@6: cd(TMPpath);
idamnjanovic@6: %%
idamnjanovic@6: 
idamnjanovic@6: %   number of different values we want to test
idamnjanovic@6: 
idamnjanovic@6: n =4;
idamnjanovic@6: 
idamnjanovic@6: lambda=zeros(1,n);
idamnjanovic@6: time = zeros(2,n);
idamnjanovic@6: psnr = zeros(2,n);
idamnjanovic@6: 
idamnjanovic@6: for i=1:n
idamnjanovic@6:     
idamnjanovic@6:     %   Here we want to test time spent and quality of denoising for
idamnjanovic@6:     %   different lambda parameters.
idamnjanovic@6:     
idamnjanovic@6:     lambda(i)=1+i*0.5;
idamnjanovic@6:     
idamnjanovic@6:     %   Defining Image Denoising Problem as Dictionary Learning Problem.
idamnjanovic@6:     
idamnjanovic@6:     SMALL.Problem = generateImageDenoiseProblem(test_image);
idamnjanovic@6:     SMALL.Problem.name=name;
idamnjanovic@6:     %%
idamnjanovic@6:     %   Use SPAMS Online Dictionary Learning Algorithm
idamnjanovic@6:     %   to Learn overcomplete dictionary (Julien Mairal 2009)
idamnjanovic@6:     
idamnjanovic@6:     %   Initialising Dictionary structure
idamnjanovic@6:     %   Setting Dictionary structure fields (toolbox, name, param, D and time)
idamnjanovic@6:     %   to zero values
idamnjanovic@6:     
idamnjanovic@6:     SMALL.DL(1)=SMALL_init_DL();
idamnjanovic@6:     
idamnjanovic@6:     %   Defining fields needed for dictionary learning
idamnjanovic@6:     
idamnjanovic@6:     SMALL.DL(1).toolbox = 'SPAMS';
idamnjanovic@6:     SMALL.DL(1).name = 'mexTrainDL';
idamnjanovic@6:     
idamnjanovic@6:     %   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
idamnjanovic@6:     
idamnjanovic@6:     SMALL.DL(1).param=struct(...
idamnjanovic@6:         'D', SMALL.Problem.initdict,...
idamnjanovic@6:         'K', SMALL.Problem.p,...
idamnjanovic@6:         'lambda', lambda(i),...
idamnjanovic@6:         'iter', 200,...
idamnjanovic@6:         'mode', 3,...
idamnjanovic@6:         'modeD', 0);
idamnjanovic@6:     
idamnjanovic@6:     %   Learn the dictionary
idamnjanovic@6:     
idamnjanovic@6:     SMALL.DL(1) = SMALL_learn(SMALL.Problem, SMALL.DL(1));
idamnjanovic@6:     
idamnjanovic@6:     %   Set SMALL.Problem.A dictionary
idamnjanovic@6:     %   (backward compatiblity with SPARCO: solver structure communicate
idamnjanovic@6:     %   only with Problem structure, ie no direct communication between DL and
idamnjanovic@6:     %   solver structures)
idamnjanovic@6:     
idamnjanovic@6:     SMALL.Problem.A = SMALL.DL(1).D;
idamnjanovic@6:     
idamnjanovic@6:     
idamnjanovic@6:     %%
idamnjanovic@6:     %   Initialising solver structure
idamnjanovic@6:     %   Setting solver structure fields (toolbox, name, param, solution,
idamnjanovic@6:     %   reconstructed and time) to zero values
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(1)=SMALL_init_solver;
idamnjanovic@6:     
idamnjanovic@6:     % Defining the parameters needed for sparse representation
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(1).toolbox='ompbox';
idamnjanovic@6:     SMALL.solver(1).name='ompdenoise';
idamnjanovic@6:     
idamnjanovic@6:     %   Denoising the image - SMALL_denoise function is similar to SMALL_solve,
idamnjanovic@6:     %   but backward compatible with KSVD definition of denoising
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(1)=SMALL_denoise(SMALL.Problem, SMALL.solver(1));
idamnjanovic@6:     
idamnjanovic@6:     
idamnjanovic@6:     %% show results %%
idamnjanovic@6:     %   This will show denoised image and dictionary for all lambdas. If you
idamnjanovic@6:     %   are not interested to see it and do not want clutter your screen
idamnjanovic@6:     %   comment following line
idamnjanovic@6:     
idamnjanovic@6:     SMALL_ImgDeNoiseResult(SMALL);
idamnjanovic@6:     
idamnjanovic@6:     
idamnjanovic@6:     time(1,i) = SMALL.DL(1).time;
idamnjanovic@6:     psnr(1,i) = SMALL.solver(1).reconstructed.psnr;
idamnjanovic@6:     
idamnjanovic@6:     clear SMALL
idamnjanovic@6: end
idamnjanovic@6: 
idamnjanovic@6: %% show time and psnr %%
idamnjanovic@6: figure('Name', 'SPAMS LAMBDA TEST');
idamnjanovic@6: 
idamnjanovic@6: subplot(1,2,1); plot(lambda, time(1,:), 'ro-');
idamnjanovic@6: title('time vs lambda');
idamnjanovic@6: subplot(1,2,2); plot(lambda, psnr(1,:), 'b*-');
idamnjanovic@6: title('PSNR vs lambda');
idamnjanovic@6: