USING LOGARITHMIC OPINION POOLING TECHNIQUES IN BAYESIAN BLIND MULTI-CHANNEL RESTORATION
Bruno Amizic, Aggelos K. Katsaggelos, Rafael Molina · 2008
In this paper we examine the use of logarithmic opinion pooling techniques to combine two observations models that are normally used in multi-channel image restoration techniques. The combined observation model is used together with simultaneous autoregression prior models for the image and blurs to define the joint distribution of image, blurs and observations. Assuming that all the unknown parameters are previously estimated we use variational techniques to approximate the posterior distribution of the real underlying image and the unknown blurs. We will examine the use of two approximations of the posterior distribution. Experimental results are used to validate the proposed approach.