Non-local Means Denoising Algorithm Derived from Combined Multi-scale Block Matching
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.
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