Edge-preserving smoothers for image processing

• 成果类型: Article
• 署名作者: Chu, CK; Glad, IK; Godtliebsen, F; Marron, JS
• 署名单位: National Tsing Hua University; UiT The Arctic University of Tromso; University of Oslo; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2670100
• 发表日期: 1998
• 页码: 526-541
• 关键词: regression; restoration

摘要

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.