fritdemo.m

基于ridgelet变化的图象去噪 含直线特征的效果好

% This Matlab script file contain examples on how to use the FRIT toolbox

% Test image (of size 257 by 257)
im = makeGauss(257, 2, 0.5);

% Wavelet parameters
wname = 'db4';
nlevels = 5;
dwtmode('per');

% FRAT and FRIT
ra = frat(im);
[ri, l, m] = frit(im, nlevels, wname);

% Reconstructed image from FRIT
imrec = ifrit(ri, l, m, wname);

% First figure
figure(1), clf, colormap('default');

subplot(2,2,1),
imagesc(im, [0, 1]), 
title('Input image'), axis square, colorbar

subplot(2,2,2), 
crange = mean(ra(:)) + std(ra(:)) * [-2, 2];
imagesc(ra, crange),
title('FRAT'), axis square, colorbar

subplot(2,2,3),
crange = mean(ri(:)) + std(ri(:)) * [-2, 2];
imagesc(ri, crange),
title('FRIT'), axis square, colorbar

subplot(2,2,4),
imagesc(imrec, [0, 1]);
title(sprintf('Reconstructed image from FRIT\n(SNR = %.2f dB)', ...	      
              SNR(im, imrec)));
axis square, colorbar


% Now compare the compression performance
ratio = 0.995;			% 99.5% compression

% Compression using FRITM
[r, l, m] = fritm(im, wname);
rc = compress(r, ratio);
rim = ifritm(rc, l, m, wname);

% Compression using WAVEDEC2
im1 = im(1:end-1,1:end-1);	% dyadic size
[w, wl] = wavedec2(im1, nlevels, wname);
wc = compress(w, ratio);
wim = waverec2(wc, wl, wname);

% Second figure
figure(2), clf, colormap('gray');

subplot(1,2,1), imagesc(wim, [0, 1]), axis square,
title(sprintf('Compressed image using WAVE2\n(Ratio = %.1f %%; SNR = %.2f dB)', ...
              100*ratio, SNR(im1, wim)));

subplot(1,2,2), imagesc(rim, [0, 1]), axis square,

title(sprintf('Compressed image using FRITM\n(Ratio = %.1f %%; SNR = %.2f dB)', ...	      
              100*ratio, SNR(im, rim)));


% Finally, the denoising problem

% Signal to noise ratio
rho = 5;

% Generate noisy image
init=2055615866; randn('seed', init); 
sig = std(im(:));
imnoisy = im + (sig/rho) * randn(size(im));

imnoisy1 = imnoisy(1:end-1,1:end-1);	% dyadic size

% Find the default thresholding values
[thr, sorh, keepapp] = ddencmp('den', 'wv', imnoisy1);

% Wavelet denoising
wdnim = wdencmp('gbl', imnoisy1, wname, nlevels, thr, sorh, keepapp);

% FRITM denoising
[r, l, m] = fritm(imnoisy, wname);
rdn = wthresh(r, sorh, thr);
rdnim = ifritm(rdn, l, m, wname);

% Third figure
figure(3), clf, colormap('gray');

subplot(2,2,1),
imagesc(im, [0, 1]), axis square
title('Original image')

subplot(2,2,2),
imagesc(imnoisy, [0, 1]), axis square
title(sprintf('Noisy image\n(SNR = %.2f dB)', ...	      
              SNR(im, imnoisy)));

subplot(2,2,3), imagesc(wdnim, [0, 1]), axis square,
title(sprintf('Denoised image using WAVE2\n(SNR = %.2f dB)', ...	      
              SNR(im1, wdnim)));

subplot(2,2,4), imagesc(rdnim, [0, 1]), axis square,
title(sprintf('Denoised image using FRITM\n(SNR = %.2f dB)', ...
              SNR(im, rdnim)));