Non-local means theory based Perona-Malik model for image denosing
文献类型：期刊论文
| 作者 | Yang, Min1; Liang, Jingkun2; Zhang, Jianhai3; Gao, Haidong1; Meng, Fanyong4; Li, Xingdong5; Song, Sung-Jin3 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2013-11-23 |
| 卷号 | 120期号:0页码:262-267 |
| 关键词 | Image denoising Non-local means Partial Differential Equation Texture detection |
| ISSN号 | 0925-2312 |
| 英文摘要 | Among 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | ANISOTROPIC DIFFUSION ; PDE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000324847100028 |
| 公开日期 | 2015-05-27 |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/13370]  |
| 专题 | 过程工程研究所_研究所（批量导入） |
| 作者单位 | 1.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 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：过程工程研究所
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