非局部MCMC采样和低秩逼近的图像去噪算法

doi:10.3969/j.issn.1001-2400.2013.06.024

Abstract

Abstract:

Combining non-local Markov-chain Monte Carlo sampling and low-rank approximation of the matrix method, an approach for image noise removal is presented. The cluster of similar patches is searched by using Markov-chain Monte Carlo sampling. The cluster matrix of similar patches is decomposed by the singular value decomposition method,and the image noise is suppressed by applying the low rank structure from decomposing. Simulation results show that the proposed method outperforms the BM3D and the non-local means (NLM)method in computational-complexity. The proposed method has a better performance in protecting image details compared with the NLM method, and has some advantages over the BM3D method in terms of visual quality.

Key words: image denoising, non-local Markov-Chain Monte Carlo method, approximation of low rank matrix, posterior probability estimate

CLC Number:

TP391

LUO Liang;FENG Xiangchu;HUO Leigang;ZHANG Xuande;WU Yulian;LI Xiaoping. Image denoising method based on non-local Markov-chain Monte Carlo sampling and low rank approximation[J].J4, 2013, 40(6): 140-146.

References

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Image denoising method based on non-local Markov-chain Monte Carlo sampling and low rank approximation

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