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WaveShrink

Soft Threshold Shrinkage Applied to Wavelet Coefficients

Calling Sequence

[xh,xwh] = WaveShrink(y,Type,L,qmf)

Parameters

Inputs:

y :

1-d signal. length(y)= 2^J Normalized to noise level 1! (See NoiseNorm)

Type :

string. Type of shrinkage applied: 'Visu','SURE','Hybrid','MinMax','MAD' Optional; default == 'Visu'

L :

Low-Frequency cutoff for shrinkage (e.g. L=4) Should have L << J!

qmf :

Quadrature Mirror Filter for Wavelet Transform Optional, Default = Symmlet 8.

Outputs :

xh :

estimate, obtained by applying soft thresholding on wavelet coefficients

xwh :

Wavelet Transform of estimate

Description

WaveShrink smooths noisy data presumed to have noise level 1 by transforming it into the wavelet domain, applying soft thresholding to the wavelet coefficients and inverse transforming.

The theory underlying these methods is described in a variety of papers by D.L. Donoho and I.M. Johnstone.

The different methods of selecting thresholds are detailed in their articles.

See also

Authors

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