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

[+] Author Affiliations
Il Kyu Eom

Pusan National University, Department of Electronics Engineering, San 30, ChangJun, Kumjeong, Busan, South Korea 609-735

Yoo Shin Kim

Pusan National University, Department of Electronics Engineering, San 30, ChangJun, Kumjeong, Busan, South Korea 609-735

Do Hoon Lee

Pusan National University, School of Computer Science and Engineering, San 30, ChangJun, Kumjeong, Busan, South Korea 609-735

J. Electron. Imaging. 16(4), 043015 (November 01, 2007). doi:10.1117/1.2804153
History: Received January 10, 2007; Revised July 06, 2007; Accepted July 06, 2007; Published November 01, 2007
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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.

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Citation

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", J. Electron. Imaging. 16(4), 043015 (November 01, 2007). ; http://dx.doi.org/10.1117/1.2804153


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