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Proceedings Paper

Improved nonlocal means method based on adaptive pre-classification for image denoising
Author(s): Shaorong He; Yaping Lin; Yonghe Liu; Junfeng Yang; Hongyan Jiang
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Paper Abstract

Nonlocal Means is an effective denoising method, which takes advantage of the fact that natural image has selfsimilarity. However, the original nonlocal means may not find enough similar candidates for some non-repetitive image blocks. In order to mitigate these drawbacks, we propose an improved nonlocal means method using adaptive preclassification in this paper. The proposed method employs the threshold-based clustering algorithm to classify noisy image blocks adaptively. Then, a rotational block matching method is adopted to find the appropriate distance measurement between two blocks in an image. Experimental results on a set of well-known standard images show that the proposed method is effective, especially when the image contains large amount of noise.

Paper Details

Date Published: 29 August 2016
PDF: 6 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100331S (29 August 2016); doi: 10.1117/12.2243996
Show Author Affiliations
Shaorong He, Hunan Univ. (China)
Yaping Lin, Hunan Univ. (China)
Yonghe Liu, Hunan Univ. (China)
Junfeng Yang, Hunan Univ. (China)
Hongyan Jiang, Hunan Branch of China Telecom (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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