A divide-and-conquer stochastic alterable direction image denoising method
A novel image denoising method based on stochastic technique is proposed in this paper. The procedure is divided into two phases: the appropriate random sampling strategy is adopted to search for similar patches, then the original image is estimated by these patches. Specifically, in order to reduce...
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| Vydáno v: | Signal processing Ročník 108; s. 90 - 101 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.03.2015
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| ISSN: | 0165-1684, 1872-7557 |
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| Abstract | A novel image denoising method based on stochastic technique is proposed in this paper. The procedure is divided into two phases: the appropriate random sampling strategy is adopted to search for similar patches, then the original image is estimated by these patches. Specifically, in order to reduce the sampling rejection rate, the observed image is decomposed into different frequency bands by 2D wavelet transform, then the similar patches are collected by alterable direction Markov-Chain Monte Carlo (MCMC) sampling with a properly chosen rejection criterion. Rather than taking the weighted average of similar patches, we use two-directional non-local (TDNL) method in order to take full use of the similarity between similar patches collected. The simulation results show that the proposed method improves the efficiency of searching similar patches. Compared with the NLM and BM3D method, our approach has lower computational complexity, better performance in protecting image details and higher visual quality, respectively.
•This paper presents a divide-and-conquer technique to search the similar patches in an image.•The original image patches are approximated by TDNL approximation method.•An effective image denoising algorithm is proposed. |
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| AbstractList | A novel image denoising method based on stochastic technique is proposed in this paper. The procedure is divided into two phases: the appropriate random sampling strategy is adopted to search for similar patches, then the original image is estimated by these patches. Specifically, in order to reduce the sampling rejection rate, the observed image is decomposed into different frequency bands by 2D wavelet transform, then the similar patches are collected by alterable direction Markov-Chain Monte Carlo (MCMC) sampling with a properly chosen rejection criterion. Rather than taking the weighted average of similar patches, we use two-directional non-local (TDNL) method in order to take full use of the similarity between similar patches collected. The simulation results show that the proposed method improves the efficiency of searching similar patches. Compared with the NLM and BM3D method, our approach has lower computational complexity, better performance in protecting image details and higher visual quality, respectively. A novel image denoising method based on stochastic technique is proposed in this paper. The procedure is divided into two phases: the appropriate random sampling strategy is adopted to search for similar patches, then the original image is estimated by these patches. Specifically, in order to reduce the sampling rejection rate, the observed image is decomposed into different frequency bands by 2D wavelet transform, then the similar patches are collected by alterable direction Markov-Chain Monte Carlo (MCMC) sampling with a properly chosen rejection criterion. Rather than taking the weighted average of similar patches, we use two-directional non-local (TDNL) method in order to take full use of the similarity between similar patches collected. The simulation results show that the proposed method improves the efficiency of searching similar patches. Compared with the NLM and BM3D method, our approach has lower computational complexity, better performance in protecting image details and higher visual quality, respectively. •This paper presents a divide-and-conquer technique to search the similar patches in an image.•The original image patches are approximated by TDNL approximation method.•An effective image denoising algorithm is proposed. |
| Author | Jia, Xi-xi Wang, Wei-wei Feng, Xiang-chu Luo, Liang |
| Author_xml | – sequence: 1 givenname: Xiang-chu orcidid: 0000-0002-3463-2060 surname: Feng fullname: Feng, Xiang-chu – sequence: 2 givenname: Liang surname: Luo fullname: Luo, Liang email: luoliang775@163.com – sequence: 3 givenname: Xi-xi surname: Jia fullname: Jia, Xi-xi – sequence: 4 givenname: Wei-wei surname: Wang fullname: Wang, Wei-wei |
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| Cites_doi | 10.1007/s11263-009-0272-7 10.1109/LSP.2005.859509 10.1006/cviu.1996.0060 10.1016/j.patcog.2011.05.002 10.1093/biomet/57.1.97 10.1007/s11263-007-0052-1 10.1109/TSP.2006.881199 10.1109/LSP.2013.2244081 10.1016/j.patcog.2009.09.023 10.1109/TCS.1987.1086066 10.3724/SP.J.1146.2010.00335 10.1109/TIP.2012.2214043 10.1016/j.sigpro.2011.03.021 10.1109/ICIP.2008.4712109 10.1109/TIP.2008.924279 10.1117/12.7973877 10.1016/0031-3203(94)90013-2 10.1109/TIP.2007.901238 10.1109/TIP.2003.819861 |
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| Title | A divide-and-conquer stochastic alterable direction image denoising method |
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