Estimating hyperparameters of mixture prior using hypothesis-testing problem and its applications to Bayesian image denoising

Il Kyu Eom; Yoo Shin Kim; Do Hoon Lee

doi:10.1117/1.2804153

1 October 2007 Estimating hyperparameters of mixture prior using hypothesis-testing problem and its applications to Bayesian image denoising

Il Kyu Eom, Yoo Shin Kim, Do Hoon Lee

Author Affiliations +

Journal of Electronic Imaging, Vol. 16, Issue 4, 043015 (October 2007). https://doi.org/10.1117/1.2804153

Abstract

We develop a spatially adaptive Bayesian image denoising method using a mixture of a Gaussian distribution and a point mass function at zero. In estimating hyperparameters, we present a simple and noniterative method. We use a hypothesis-testing technique in order to estimate the mixing parameter, the Bernoulli random variable. Based on the estimated mixing parameter, the variance for a clean signal is obtained by using the maximum generalized marginal likelihood (MGML) estimator. We simulate our denoising method using both orthogonal wavelet and dual-tree complex wavelet transforms and compare our algorithm to well-known denoising schemes. Experimental results show that the proposed method can generate good denoising results.

©(2007) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Il Kyu Eom, Yoo Shin Kim, and Do Hoon Lee "Estimating hyperparameters of mixture prior using hypothesis-testing problem and its applications to Bayesian image denoising," Journal of Electronic Imaging 16(4), 043015 (1 October 2007). https://doi.org/10.1117/1.2804153

Published: 1 October 2007

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