Edge-Preserving Smoothers for Image Processing
针对经典平滑器模糊边缘的问题，讨论了sigma滤波方法，并提出了基于运行M估计的改进，适用于标准滤波与贝叶斯-马尔可夫随机场分析之间的场景。
Abstract Classical smoothers have limited usefulness in image processing, because sharp "edges" tend to be blurred. There is a literature on edge-preserving smoothers, but these all require moderately large "smooth stretches." 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 processing, the methods have a niche between standard filtering approaches and Bayes–Markov random-field analysis. Key Words: Image processingJump-preserving smoothingRobust M estimation.