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Non-local means theory based Perona-Malik model for image denosing
Yang, Min1; Liang, Jingkun2; Zhang, Jianhai3; Gao, Haidong1; Meng, Fanyong4; Li, Xingdong5; Song, Sung-Jin3
2013-11-23
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume120Issue:0Pages:262-267
AbstractAmong various kinds of image denoising methods, the Perona-Malik model is a representative Partial Differential Equation based (PDE-based) algorithm which effectively removes the noise as well as having edge enhancement simultaneously through anisotropic diffusion controlled by the diffusion coefficient. However, the unstable behavior of the Perona-Malik model introduces staircasing artifacts in the processed images. To realize less diffusion in the texture region and to get more smooth in flat region while implementing image denoising, we propose an improved Perona-Malik model based on non-local means theory, which assumes that the image contains an extensive amount of self-similarity and uses the similarity between the region around the center pixel and the region outside the center pixel to give a more reasonable description of the image. The improved algorithm is applied on numerical simulation and practical images, and the quantitative analyzing results prove that the modified anisotropic diffusion model can preserve textures effectively while ruling out the noise, meanwhile, the staircasing effects are decreased obviously. (c) 2013 Elsevier B.V. All rights reserved.
KeywordImage Denoising Non-local Means Partial Differential Equation Texture Detection
SubtypeArticle
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2012.08.063
Indexed BySCI
Language英语
WOS KeywordANISOTROPIC DIFFUSION ; PDE
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000324847100028
Citation statistics
Cited Times:26[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/13370
Collection研究所(批量导入)
Affiliation1.Beijing Univ Aeronaut & Astronaut, Sch Mech Engn & Automat, Beijing 100191, Peoples R China
2.Shijiazhuang Vocat Technol Insititute, Dept Informat Technol, Shijiazhuang 050081, Hebei, Peoples R China
3.Sungkyunkwan Univ, Sch Mech Engn, Suwon 440746, South Korea
4.Chinese Acad Sci, Inst Proc Engn, State Key Lab Multiphase Complex Syst, Beijing 100190, Peoples R China
5.Natl Inst Metrol, Div Metrol Ionizing Radiat & Med, Beijing 100013, Peoples R China
Recommended Citation
GB/T 7714
Yang, Min,Liang, Jingkun,Zhang, Jianhai,et al. Non-local means theory based Perona-Malik model for image denosing[J]. NEUROCOMPUTING,2013,120(0):262-267.
APA Yang, Min.,Liang, Jingkun.,Zhang, Jianhai.,Gao, Haidong.,Meng, Fanyong.,...&Song, Sung-Jin.(2013).Non-local means theory based Perona-Malik model for image denosing.NEUROCOMPUTING,120(0),262-267.
MLA Yang, Min,et al."Non-local means theory based Perona-Malik model for image denosing".NEUROCOMPUTING 120.0(2013):262-267.
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