📄 mefig214.m
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% mefig214 -- Segmented vs. Ordinary de-noising; object "Cusp"
%
% In this display we compare segmented de-noising of figure 2.13
% with traditional de-noising using periodized nearly-symmetric wavelets
% having 8 vanishing moments.
%
% De-noising in a non-segmented wavelet transform domain leads to
% "pulling down" of peak values. Such distortion can be avoided
% avoided by de-noising with ideally-selected segmentation point.
%
% Note -- this script uses results calculated in mefig213.m
%
%
global yCusp Cusp SmoothCusp
global L
if isempty(yCusp)
warndlg('Please Run figure 2.13 first and then run this figure');
return;
end
%
qmf = MakeONFilter('Symmlet',8);
wyCusp = FWT_PO(yCusp,L,qmf);
shCusp = MultiVisu(wyCusp,L);
sbCusp = IWT_PO(shCusp,L,qmf);
%
%clf;
absc = (1:length(Cusp)) ./ length(Cusp);
object = 5*Cusp.^8;
%subplot(111)
plot(absc, object,':'); hold on;
plot(absc,sbCusp, '-.');
plot(absc,SmoothCusp,'-'); hold off
title('2.14 Object Cusp (..) Unsegmented (-.) and Segmented (-) DeNoising')
sum((object - SmoothCusp).^2)
sum((object - sbCusp).^2)
%
% Prepared for the paper Minimum Entropy Segmentation
% Copyright (c) 1994 David L. Donoho
%
%% Part of Wavelab Version 850% Built Tue Jan 3 13:20:41 EST 2006% This is Copyrighted Material% For Copying permissions see COPYING.m% Comments? e-mail wavelab@stat.stanford.edu
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