Bayesian wavelet-based image estimation using noninformative priors

Mario A. T. Figueiredo; Robert D. Nowak

doi:10.1117/12.351304

25 June 1999 Bayesian wavelet-based image estimation using noninformative priors

Mario A. T. Figueiredo, Robert D. Nowak

Proceedings Volume 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems; (1999) https://doi.org/10.1117/12.351304
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

Citation Download Citation

Mario A. T. Figueiredo and Robert D. Nowak "Bayesian wavelet-based image estimation using noninformative priors", Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); https://doi.org/10.1117/12.351304

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