Adaptive wavelet thresholding for multichannel signal estimation

Ian C. Atkinson; Farzad Kamalabadi; Douglas L. Jones; Minh N. Do

doi:10.1117/12.506397

13 November 2003 Adaptive wavelet thresholding for multichannel signal estimation

Ian C. Atkinson, Farzad Kamalabadi, Douglas L. Jones, Minh N. Do

Proceedings Volume 5207, Wavelets: Applications in Signal and Image Processing X; (2003) https://doi.org/10.1117/12.506397
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

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.

Citation Download Citation

Ian C. Atkinson, Farzad Kamalabadi, Douglas L. Jones, and Minh N. Do "Adaptive wavelet thresholding for multichannel signal estimation", Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); https://doi.org/10.1117/12.506397

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