Soft Threshold Shrinkage Applied to Wavelet Coefficients
[xh,xwh] = WaveShrink(y,Type,L,qmf)
1-d signal. length(y)= 2^J Normalized to noise level 1! (See NoiseNorm)
string. Type of shrinkage applied: 'Visu','SURE','Hybrid','MinMax','MAD' Optional; default == 'Visu'
Low-Frequency cutoff for shrinkage (e.g. L=4) Should have L << J!
Quadrature Mirror Filter for Wavelet Transform Optional, Default = Symmlet 8.
estimate, obtained by applying soft thresholding on wavelet coefficients
Wavelet Transform of estimate
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.