海南师范大学学报（自然科学版）2024，Vol.37Issue(1)：46-55,10.DOI:10.12051/j.issn.1674-4942.2024.01.006
联合多尺度块匹配的非局部均值去噪算法
Non-local Means Denoising Algorithm Derived from Combined Multi-scale Block Matching
摘要
Abstract
Aiming at the problems that Non-Local Means(NLM)algorithm for image denoising tends to produce artifacts and smooth details,in this paper,multi-scale matching combination of image blocks was adopted to measure the similarity between pixels,which can improve the denoising performance of NLM algorithm.First,two similarity metrics(weighted Eu-clidean distance and Euclidean distance)and image block size used in NLM were studied and analyzed.Secondly,the whole image was partitioned into flat region and structural region by introducing its feature information and using K-means clustering method.For pixels in each category,the smooth weights were calculated by combining the matching of two image blocks in different sizes.Finally,the optimal choice for filtering parameter was given.Experimental results show that the proposed method outperformed the classical NLM algorithm in terms of noise removal and detail preservation and also has advantages over other improved NLM algorithms.关键词
图像去噪/非局部均值/局部特征/多尺度块匹配Key words
image denoising/Non-Local Means/local feature/multi-scale block matching分类
信息技术与安全科学引用本文复制引用
陈浩宇,许光宇..联合多尺度块匹配的非局部均值去噪算法[J].海南师范大学学报（自然科学版）,2024,37(1):46-55,10.基金项目
国家自然科学基金项目(61471004) （61471004）
安徽理工大学博士基金(ZX942) （ZX942）
安徽理工大学研究生创新基金项目(2022CX2125) （2022CX2125）
