comparison examples/Image Denoising/SMALL_ImgDenoise_DL_test_SPAMS_lambda.m @ 6:f72603404233

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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.
+%   This example can be used to test SPAMS for different values of
+%   parameter lambda. In no way it represents extensive testing of image
+%   denoising. It should only give an idea how SMALL structure can be used
+%   for testing.
+%
+%   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 =4;
+
+lambda=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 lambda parameters.
+    
+    lambda(i)=1+i*0.5;
+    
+    %   Defining Image Denoising Problem as Dictionary Learning Problem.
+    
+    SMALL.Problem = generateImageDenoiseProblem(test_image);
+    SMALL.Problem.name=name;
+    %%
+    %   Use SPAMS Online Dictionary Learning Algorithm
+    %   to Learn overcomplete dictionary (Julien Mairal 2009)
+    
+    %   Initialising Dictionary structure
+    %   Setting Dictionary structure fields (toolbox, name, param, D and time)
+    %   to zero values
+    
+    SMALL.DL(1)=SMALL_init_DL();
+    
+    %   Defining fields needed for dictionary learning
+    
+    SMALL.DL(1).toolbox = 'SPAMS';
+    SMALL.DL(1).name = 'mexTrainDL';
+    
+    %   Type 'help mexTrainDL in MATLAB prompt for explanation of parameters.
+    
+    SMALL.DL(1).param=struct(...
+        'D', SMALL.Problem.initdict,...
+        'K', SMALL.Problem.p,...
+        'lambda', lambda(i),...
+        'iter', 200,...
+        'mode', 3,...
+        'modeD', 0);
+    
+    %   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 sparse representation
+    
+    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));
+    
+    
+    %% show results %%
+    %   This will show denoised image and dictionary for all lambdas. If you
+    %   are not interested to see it 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;
+    
+    clear SMALL
+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');
+