Image de-noising based on weight improved non-local means filtering algorithm

Chen-long Guo; Yu Tian; Wang Wei; Haiyan Zheng

doi:10.1117/12.2514800

29 October 2018 Image de-noising based on weight improved non-local means filtering algorithm

Chen-long Guo, Yu Tian, Wang Wei, Haiyan Zheng

Proceedings Volume 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence; 108360U (2018) https://doi.org/10.1117/12.2514800
Event: 2018 International Conference on Image, Video Processing and Artificial Intelligence, 2018, Shanghai, China

Abstract

An improved non-local means filter algorithm is proposed. The common NLM algorithm only considers the Euclidean distance between pixel values as the calculation standard of weights, neglects the spatial position relationship of pixels and the similarity of texture details between image blocks, which results in the distortion of image structure after filtering, and the edge information is missing. To solve this problem, the author uses the spatial position of pixels in the image to improve the Euclidean distance. At the same time, the structural similarity index measurement (SSIM) is used to measure the similarity of neighbourhood image blocks to obtain the similarity weight, using this weight, the Euclidean distance of the image block is weighted again to reduce the weight of image blocks with low structural similarity. At the same time, the weight of the image blocks with high structural similarity is increased to achieve the ability to maintain the edge information. The experimental results show that the proposed algorithm effectively maintains the edge and detail of the image, and is superior to the conventional NLM algorithm in terms of PSNR and SSIM indicators.

Citation Download Citation

Chen-long Guo, Yu Tian, Wang Wei, and Haiyan Zheng "Image de-noising based on weight improved non-local means filtering algorithm", Proc. SPIE 10836, 2018 International Conference on Image and Video Processing, and Artificial Intelligence, 108360U (29 October 2018); https://doi.org/10.1117/12.2514800

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