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On The Estimation Of Hyperparameters In Bayesian Approach Of Solving Inverse Problems

By Aii Iohammad Djafari

Abstract

In this paper wc propose a new view on the estimation of the hyperparameters (the parameters of the prior law) when a Bayesian approach with Maximum Entropy (ME) priors is used to solve the inverse problems which arise in signal and image reconstruction and restoration problems. In particular we compare two methods; the Expectation Maximization (EM) algorithm who aims to find the Marginalized Maximum Likelihood (MMb) estimate and the Generalized Maximum Likelihood (GML). Some simulation results with application in image restoration are provided t show the performances of the GML method

Year: 1993
OAI identifier: oai:CiteSeerX.psu:10.1.1.19.8915
Provided by: CiteSeerX
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