Bayesian image restoration, using configurations
by T. L. Thorarinsdottir
Research Reports
Number 477 (June 2006)
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 $3 \times 3$ and $5 \times 5$ configurations and examples of the performance of the procedure are given.
This primarily serves as Thiele Research Reports number 8-2006, but was also published in Research Reports