EDGE-PRESERVING SMOOTHERS FOR IMAGE-PROCESSING

Classical smoothers have limited usefulness in image processing, becau se sharp ''edges'' tend to be blurred. There is a literature on edge-p reserving smoothers, but these all require moderately large ''smooth s tretches.'' Here we discuss an approach to this problem called ''sigma filtering'' and propose an improvement based on running M estimation. Both computational and theoretical aspects are developed. For image p rocessing, the methods have a niche between standard filtering approac hes and Bayes-Markov random-field analysis.