idamnjanovic@6: %%  Pierre Villars Example
idamnjanovic@6: %   This example is based on the experiment suggested by Professor Pierre
idamnjanovic@6: %   Vandergheynst on the SMALL meeting in Villars.
idamnjanovic@6: %   The idea behind is to use patches from source image as a dictonary in
idamnjanovic@6: %   which we represent target image using matching pursuit algorithm.
idamnjanovic@6: %   Calling Pierre_Problem function to get src image to be used as dictionary
idamnjanovic@6: %   and target image to be represented using MP with 3 paches from source image
idamnjanovic@6: %
idamnjanovic@6: %
idamnjanovic@6: %   Ivan Damnjanovic 2010
idamnjanovic@6: %%
idamnjanovic@6: 
idamnjanovic@6: clear all;
idamnjanovic@6: 
idamnjanovic@6: %   Defining the Problem structure
idamnjanovic@6: 
idamnjanovic@6: SMALL.Problem = Pierre_Problem();
idamnjanovic@6: 
idamnjanovic@6: %   Show original image and image that is used as a dictionary
idamnjanovic@6: figure('Name', 'Original and Dictionary Image');
idamnjanovic@6: 
idamnjanovic@6: subplot(1,2,1); imshow(SMALL.Problem.imageTrg/SMALL.Problem.maxval);
idamnjanovic@6: title('Original Image');
idamnjanovic@6: subplot(1,2,2); imshow(SMALL.Problem.imageSrc/SMALL.Problem.maxval);
idamnjanovic@6: title('Dictionary image:');
idamnjanovic@6: 
idamnjanovic@6: %   Using ten different dictionary sizes. First dictionary will contain all
idamnjanovic@6: %   patches from the source image and last one will have only
idamnjanovic@6: %   num_src_patches/2^9 atoms representing equidistant patches taken from
idamnjanovic@6: %   the source image.
idamnjanovic@6: 
idamnjanovic@6: n =10;
idamnjanovic@6: dictsize=zeros(1,n);
idamnjanovic@6: time = zeros(1,n);
idamnjanovic@6: psnr = zeros(1,n);
idamnjanovic@6: 
idamnjanovic@6: for i=1:n
idamnjanovic@6:     
idamnjanovic@6:     
idamnjanovic@6:     %   Set reconstruction function
idamnjanovic@6:     
idamnjanovic@6:     SMALL.Problem.reconstruct=@(x) Pierre_reconstruct(x, SMALL.Problem);
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(i)=SMALL_init_solver;
idamnjanovic@6:     
idamnjanovic@6:     %   Defining the parameters sparse representation
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(i).toolbox='SMALL';
idamnjanovic@6:     SMALL.solver(i).name='SMALL_MP';
idamnjanovic@6:     
idamnjanovic@6:     %   Parameters needed for matching pursuit (max number of atoms is 3
idamnjanovic@6:     %   and residual error goal is 1e14
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(i).param=sprintf('%d, 1e-14',3);
idamnjanovic@6:     
idamnjanovic@6:     % Represent the image using the source image patches as dictionary
idamnjanovic@6:     
idamnjanovic@6:     SMALL.solver(i)=SMALL_solve(SMALL.Problem, SMALL.solver(i));
idamnjanovic@6:     
idamnjanovic@6:     
idamnjanovic@6:     dictsize(1,i) = size(SMALL.Problem.A,2);
idamnjanovic@6:     time(1,i) = SMALL.solver(i).time;
idamnjanovic@6:     psnr(1,i) = SMALL.solver(i).reconstructed.psnr;
idamnjanovic@6:     
idamnjanovic@6:     %   Set new SMALL.Problem.A dictionary taking every second patch from
idamnjanovic@6:     %   previous dictionary
idamnjanovic@6:     
idamnjanovic@6:     SMALL.Problem.A=SMALL.Problem.A(:,1:2:dictsize(1,i));
idamnjanovic@6:     
idamnjanovic@6:     
idamnjanovic@6:     %%  show reconstructed image %%
idamnjanovic@6:     figure('Name', sprintf('dictsize=%d', dictsize(1,i)));
idamnjanovic@6:     
idamnjanovic@6:     imshow(SMALL.solver(i).reconstructed.image/SMALL.Problem.maxval);
idamnjanovic@6:     title(sprintf('Reconstructed image, PSNR: %.2f dB in %.2f s',...
idamnjanovic@6:         SMALL.solver(i).reconstructed.psnr, SMALL.solver(i).time ));
idamnjanovic@6:     
idamnjanovic@6:     
idamnjanovic@6: end
idamnjanovic@6: 
idamnjanovic@6: %%  plot time and psnr given dictionary size %%
idamnjanovic@6: figure('Name', 'time and psnr');
idamnjanovic@6: 
idamnjanovic@6: subplot(1,2,1); plot(dictsize(1,:), time(1,:), 'ro-');
idamnjanovic@6: title('Time vs number of source image patches used');
idamnjanovic@6: subplot(1,2,2); plot(dictsize(1,:), psnr(1,:), 'b*-');
idamnjanovic@6: title('PSNR vs number of source image patches used');