diff util/SMALL_ImgDeNoiseResult.m @ 8:33850553b702

(none)

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/util/SMALL_ImgDeNoiseResult.m	Mon Mar 22 10:56:54 2010 +0000
@@ -0,0 +1,38 @@
+function SMALL_ImgDeNoiseResult(SMALL)
+%   Ivan Damnjanovic 2010
+%   Function gets as input SMALL structure and plots  Image Denoise
+%   results: Original Image, Noisy Image and for learned dictionaries and 
+%   denoised images 
+
+
+figure('Name', sprintf('Image %s (training set size- %d, sigma - %d)',SMALL.Problem.name, SMALL.Problem.n, SMALL.Problem.sigma));
+
+m=size(SMALL.solver,2)+1;
+maxval=SMALL.Problem.maxval;
+im=SMALL.Problem.Original;
+imnoise=SMALL.Problem.Noisy;
+
+subplot(2, m, 1); imshow(im/maxval);
+title('Original image');
+
+subplot(2,m,m+1); imshow(imnoise/maxval);
+title(sprintf('Noisy image, PSNR = %.2fdB', 20*log10(maxval * sqrt(numel(im)) / norm(im(:)-imnoise(:))) ));
+
+for i=2:m
+    params=SMALL.solver(i-1).param;
+    subplot(2, m, i); imshow(SMALL.solver(i-1).reconstructed.Image/maxval);
+    title(sprintf('%s Denoised image, PSNR: %.2f dB in %.2f s',...
+        SMALL.DL(i-1).name, SMALL.solver(i-1).reconstructed.psnr, SMALL.solver(i-1).time ));
+    if strcmpi(SMALL.DL(i-1).name,'ksvds')
+        D = kron(SMALL.Problem.basedict{2},SMALL.Problem.basedict{1})*SMALL.DL(i-1).D;
+    else
+        D = SMALL.DL(i-1).D;
+    end
+    dictimg = showdict(D,params.blocksize,...
+        round(sqrt(size(D,2))),round(sqrt(size(D,2))),'lines','highcontrast');
+    
+    subplot(2,m,m+i);imshow(imresize(dictimg,2,'nearest'));
+    title(sprintf('%s dictionary in %.2f s',...
+        SMALL.DL(i-1).name, SMALL.DL(i-1).time));
+    
+end
\ No newline at end of file