📄 toufig10.m
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% toufig10 -- Tour Figure 10: Noisy Differentiation
%
% Here we illustrate the use of wavelets for noisy differentiation.
% (Panel a) displays object Bumps, (panel b) its noisy cumulative,
% and (Panel c) a naive inversion based on differencing. The naive inversion
% is very noisy, and displays only a hint of the structure of the underlying
% function.
%
% We also display a wavelet-shrinkage reconstruction, in which noise is
% suppressed while the structure remains.
%
% The wavelet-shrinkage reconstruction goes as follows
%
% 1. Transform the naive reconstruction into the wavelet domain.
% 2. Apply level-dependent thresholds which scale inversely with
% resolution.
% 3. Return to the wavelet domain.
%
% Coiflets with 3 vanishing moments are used.
%
global Bumps
global w wb ws
%
zBumps = cumsum(Bumps);
N = length(Bumps);
t = (0:(N-1))/N;
x = zBumps + WhiteNoise(zBumps);
z = diff([0 x]);
%
QMF_Filter = MakeONFilter('Coiflet',3);
w = FWT_PO(z, 5,QMF_Filter);
ws = InvShrink(w,5,4,1);
zrec = IWT_PO(ws,5,QMF_Filter);
%
wb = FWT_PO(Bumps,5,QMF_Filter);
%
% clf;
subplot(221); plot(t,Bumps); title('10 (a) Object Bumps')
subplot(222); plot(t,x); title('10 (b) Noisy Cumulative')
subplot(223); plot(t,z); title('10 (c) Difference of Noisy Cumulative')
subplot(224); plot(t,zrec); title('10 (d) WVD Reconstruction')
% Revision History
% 10/1/05 AM Changing the name of the variable QMF
%% 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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