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Proceedings Paper

Adaptive wavelet thresholding for multichannel signal estimation
Author(s): Ian C. Atkinson; Farzad Kamalabadi; Douglas L. Jones; Minh N. Do
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Paper Abstract

In this paper, we illustrate how a recently proposed wavelet-based estimation scheme for 2-D multichannel signals can utilize an overcomplete wavelet expansion or the BayesShrink adaptive wavelet-domain threshold to improve estimation results. The existing technique approximates the optimal estimator using a DFT and an orthonormal 2-D DWT to efficiently decorrelate the signal in both channel and space, and a wavelet-domain threshold to suppress the noise. Although this technique typically yields signal-to-noise ratio (SNR) gains of over 12 dB, results can be improved 1 to 1.5 dB by replacing the critically-sampled wavelet expansion with an overcomplete wavelet expansion. In addition, provided that the detail subbands of the original signal channels each obey a generalized Gaussian distribution, average channel SNR gains can be improved 3 dB or more using the BayesShrink adaptive wavelet-domain threshold.

Paper Details

Date Published: 13 November 2003
PDF: 12 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.506397
Show Author Affiliations
Ian C. Atkinson, Univ. of Illinois/Urbana-Champaign (United States)
Farzad Kamalabadi, Univ. of Illinois/Urbana-Champaign (United States)
Douglas L. Jones, Univ. of Illinois/Urbana-Champaign (United States)
Minh N. Do, Univ. of Illinois/Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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