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Proceedings Paper

Bayesian wavelet-based image estimation using noninformative priors
Author(s): Mario A. T. Figueiredo; Robert D. Nowak
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Paper Abstract

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. Most schemes use arbitrary thresholding nonlinearities with ad hoc parameters, or employ computationally expensive adaptive procedures. We overcome these deficiencies with a new wavelet-based denoising is a step towards objective Bayesian wavelet-based denoising. The result is a remarkably simple fixed non-linear shrinkage/thresholding rule which performs better than other more computationally demanding methods.

Paper Details

Date Published: 25 June 1999
PDF: 12 pages
Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); doi: 10.1117/12.351304
Show Author Affiliations
Mario A. T. Figueiredo, Instituto de Telecomunicacoes (Portugal) and Instituto Superior Tecnico (Portugal)
Robert D. Nowak, Michigan State Univ. (United States)


Published in SPIE Proceedings Vol. 3816:
Mathematical Modeling, Bayesian Estimation, and Inverse Problems
Françoise J. Prêteux; Ali Mohammad-Djafari; Edward R. Dougherty, Editor(s)

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