On the performance of stack filters and vector detection in image restoration

Two techniques for image restoration are compared in this paper. One is a technique based on the theory of optimal adaptive stack filtering; the other is a recently developed vector detection approach to image restoration. The primary difference between these two techniques is that the optimal detection technique exploits multilevela priori information, while the stack filter uses only single level zero crossing information.The design constraints for stack filters and vector detection are similar. Both approaches rely on the existence of a training sequence for the image source in order to obtain optimal processing. Adaptive stack filters do, however, require a training set of the noise while the optimal detection approach only needs a multivariate parametric representation.The image-restoration performance of these two methods is compared in a signal dependent noise environment characterizing imaging systems with speckle, film-grain, and Poisson shot noise. Comparisons are made using the mean absolute error measure as well as a subjective measure.This research was sponsored in part by the A. I. DuPont Institute.