Share Email Print
cover

Proceedings Paper

Adaptive wavelet thresholding for multichannel signal estimation
Author(s): Ian C. Atkinson; Farzad Kamalabadi; Douglas L. Jones; Minh N. Do
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top