Two-dimensional Bayesian estimate of images

Abstract

A dynamic model for pictorial data that can be represented by a random field of an exponential autocorrelation function is developed. A partial difference equation describes the dynamic model and is used to realize a two-dimensional recursive filter that gives a Bayesian-estimate of the pictorial data from a noisy observation of the data. It is assumed that the noise is additive, white, and uncorrelated with the signal. Practical application of the estimation technique is illustrated by applying the results to enhance several pictures. A comparison of this technique and its one-dimensional counterpart (Kalman filter) is made, and generalization of the estimation technique to other autoregressive sources is considered.