BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS

Thordis Linda Thorarinsdottir

Abstract

In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.

Keywords
Bayesian image analysis; configurations; digital image analysis; salt and pepper noise; stochastic geometry

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DOI: 10.5566/ias.v25.p129-143

Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)