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Journal of Electronic Imaging

Estimating hyperparameters of mixture prior using hypothesis-testing problem and its applications to Bayesian image denoising
Author(s): Il Kyu Eom; Yoo Shin Kim; Do Hoon Lee
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Paper 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.

Paper Details

Date Published: 1 October 2007
PDF: 8 pages
J. Electron. Imag. 16(4) 043015 doi: 10.1117/1.2804153
Published in: Journal of Electronic Imaging Volume 16, Issue 4
Show Author Affiliations
Il Kyu Eom, Pusan National Univ. (South Korea)
Yoo Shin Kim, Pusan National Univ. (South Korea)
Do Hoon Lee, Pusan National Univ. (South Korea)

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