A Two-Step Regularization Framework for Non-Local Means
As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computin...
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| Published in | Journal of computer science and technology Vol. 29; no. 6; pp. 1026 - 1037 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.11.2014
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1000-9000 1860-4749 |
| DOI | 10.1007/s11390-014-1487-9 |
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| Abstract | As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the fl-amework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results. |
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| AbstractList | As an effective patch-based denoising method, non-local means (NLM) method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The implementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the framework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results. As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the fl-amework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results. |
| Author | 孙忠贵 陈松灿 乔立山 |
| AuthorAffiliation | Department of Mathematics Science, Liaocheng University, Liaocheng 252000, China College of Computer Science and Technology, Nanjing University of Aeronautics ~z Astronautics, Nanjing 210016, China |
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| Cites_doi | 10.1109/ICCV.1998.710815 10.1007/978-3-540-68636-1_5 10.1109/34.56205 10.1109/TSP.2006.881199 10.1109/LSP.2009.2038956 10.1109/83.370679 10.1109/ICCV.1999.790383 10.1109/78.370615 10.1007/s10851-009-0169-7 10.1007/978-3-540-88690-7_5 10.1109/TIT.2007.903124 10.1145/358198.358222 10.1007/978-3-642-03641-5_26 10.1016/j.patcog.2009.12.022 10.1109/TMI.2012.2211378 10.1023/A:1007963824710 10.1109/TIP.2012.2216278 10.1016/j.optcom.2012.07.045 10.3233/IDA-2010-0433 10.1109/TIP.2007.901238 10.1007/978-3-642-81929-2 10.3934/ipi.2011.5.511 10.1002/9780470434697 10.1109/LSP.2013.2245322 10.1109/TPAMI.2006.64 10.1109/TPAMI.2010.114 10.1109/LSP.2012.2217329 10.1007/978-3-642-03641-5_25 10.1137/090773908 10.1016/0167-2789(92)90242-F 10.1109/CVPR.2005.38 10.1007/978-3-540-89197-0_36 |
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| Copyright | Springer Science+Business Media New York 2014 Springer Science+Business Media New York 2014. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DOI | 10.1007/s11390-014-1487-9 |
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| Keywords | non-local means framework non-local median image denoising regularization |
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| Notes | 11-2296/TP As an effective patch-based denoising method, non-local means （NLM） method achieves favorable denoising performance over its local counterparts and has drawn wide attention in image processing community. The in, plementation of NLM can formally be decomposed into two sequential steps, i.e., computing the weights and using the weights to compute the weighted means. In the first step, the weights can be obtained by solving a regularized optimization. And in the second step, the means can be obtained by solving a weighted least squares problem. Motivated by such observations, we establish a two-step regularization framework for NLM in this paper. Meanwhile, using the fl-amework, we reinterpret several non-local filters in the unified view. Further, taking the framework as a design platform, we develop a novel non-local median filter for removing salt-pepper noise with encouraging experimental results. non-local means, non-local median, framework, image denoising, regularization Zhong-Gui Sun Song-Can Chen and Li-Shan Qiao（1Department of Mathematics Science, Liaocheng University, Liaocheng 252000, China 2 College of Computer Science and Technology, Nanjing University of Aeronautics ~z Astronautics, Nanjing 210016, China） ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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In Proc. the 7th International Conference on Computer Vision (ICCV), Sept. 1999, pp.1033–1038. Peyré G, Bougleux S, Cohen L. Non-local regularization of inverse problems. In Lecture Notes in Computer Science 5304, Forsyth D, Torr P, Zisserman A (eds.), Springer-Verlag, 2008, pp.57–68. Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms. Physica D Nonlinear Phenomena, 1992, 60(1/2/3/4): 259–268. SunJZhaoWXueJShenZShenYClustering with feature order preferencesIntelligent Data Analysis201014479495 SunZChenSAnalysis of non-local Euclidean medians and its improvementIEEE Signal Processing Letters201320430330610.1109/LSP.2013.2245322 Bovik A. Handbook of Image and Video Processing. Academic Press, 2000. 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| References_xml | – reference: Efros A A, Leung T K. Texture synthesis by non-parametric sampling. In Proc. the 7th International Conference on Computer Vision (ICCV), Sept. 1999, pp.1033–1038. – reference: SunZChenSAnalysis of non-local Euclidean medians and its improvementIEEE Signal Processing Letters201320430330610.1109/LSP.2013.2245322 – reference: YangZJacobMNonlocal regularization of inverse problems: A unified variational frameworkIEEE Transactions on Image Processing20132283192320310.1109/TIP.2012.2216278 – reference: HwangHHaddadRAAdaptive median filters: New algorithms and resultsIEEE Transactions on Image Processing19954449950210.1109/83.370679 – reference: Arias P, Caselles V, Sapiro G. A variational framework for non-local image inpainting. In Proc. the 7th Int. Conf. Energy Minimization Methods in Computer Vision and Pattern Recognition, Aug. 2009, pp.345–358. – reference: VigneshRByungTOKuoCCJFast non-local means (NLM) computation with probabilistic early terminationIEEE Signal Processing Letters201017327728010.1109/LSP.2009.2038956 – reference: LuoPZhanGHeQShiZLuKOn defining partition entropy by inequalitiesIEEE Transactions on Information Theory20075393233323910.1109/TIT.2007.903124054557942417688 – reference: AharonMEladMBrucksteinAK-SVD: An algorithm for designing of overcomplete dictionaries for sparse representationIEEE Trans. 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| SubjectTerms | Artificial Intelligence Communities Computer Science Data Structures and Information Theory Image processing Information Systems Applications (incl.Internet) Least squares method NLM Noise Noise reduction Optimization Platforms Regular Paper Regularization Software Engineering Theory of Computation 中值滤波器 去噪方法 图像处理 最小二乘问题 框架 正规化 降噪性能 |
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