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| Non-local Means Denoising Derived from Structure-adapted Block Matching |
| Zhong Ying Yang Xue-zhi Tang Yi-ming Liu Can-jun Yue Feng |
| School of Computer & Information, Hefei University of Technology, Hefei 230009, China |
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Abstract A distinct non-local means denoising algorithm derived from structure-adapted block matching is proposed in this paper. Multi-scale matching of image blocks is adopted to measure similarity of local structures, which can deal with complex structural characteristics effectively and subsequently improve denoising performance. To begin with, structural region (including edges and textures) and flat region are divided by introducing Coefficient of Variation (CV) characteristics and the CV-Kmeans region classification algorithm is proposed. Furthermore, the size of similar block is adaptively selected based on average Euclidean distance between blocks in structural regions. Finally, a new non-local means algorithm is proposed to remove noise. Compared to the classical non-local means algorithm, the improved algorithm using patch probabilistic similarity and the adapted non-local means denoising algorithm, experimental results show that the proposed algorithm increases denoising performance and especially demonstrates a distinct advantage in texture images.
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Received: 22 January 2013
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Corresponding Authors:
Yang Xue-zhi
E-mail: hfut.cv@gmail.com
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2013, Vol. 35 
