📄 scfig22.m
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% scfig22 -- Short Course 22: Wavelet Packet DeNoising
%
% In this figure, we DeNoise the signals in Figure 21
% by the following procedure
%
% 1. Select a Wavelet Packet Basis minimizing the SURE
% entropy criterion. This is a criterion that estimates
% the quality of thresholding reconstruction
% in a given basis.
% 2. Apply soft thresholding in the adaptively selected basis.
% 3. Reconstruct an estimate of the signal from its thresholded
% coefficients
%
% The results are competitive with wavelet-based methods on the two
% signals Bumps and Doppler that we previously restored by wavelet
% methods. The results are much better than wavelet methods on the
% two other signals.
%
global tt yQuad yMish vBumps vDoppler
%
D = 5;
QCoif3 = MakeONFilter('Coiflet',3);
%
wQuad = WPDeNoise(yQuad, D,QCoif3);
wMish = WPDeNoise(yMish, D,QCoif3);
wBumps = WPDeNoise(vBumps, D,QCoif3);
wDoppler = WPDeNoise(vDoppler,D,QCoif3);
%
% clf;
versaplot(221,tt,wQuad, [],' 22 (a) WP Denoising QuadChirp',[],[])
versaplot(222,tt,wMish, [],' 22 (b) WP Denoising MishMash' ,[],[])
versaplot(223,tt,wBumps, [],' 22 (c) WP Denoising Bumps' ,[],[])
versaplot(224,tt,wDoppler,[],' 22 (d) WP Denoising Doppler' ,[],[])
%% Part of Wavelab Version 850% Built Tue Jan 3 13:20:42 EST 2006% This is Copyrighted Material% For Copying permissions see COPYING.m% Comments? e-mail wavelab@stat.stanford.edu
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