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Fast and Error-Bounded Space-Variant Bilateral Filtering

文献类型：期刊论文

作者Yuan, Meng-Ke1,2; Dai, Long-Quan3; Yan, Dong-Ming1,2; Zhang, Li-Qiang4; Xiao, Jun2; Zhang, Xiao-Peng1,2
刊名JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期2019-05-01
卷号34期号:3页码:550-568
关键词error-bounded acceleration edge-aware smoothing space-variant bilateral filtering
ISSN号1000-9000
DOI10.1007/s11390-019-1926-8
通讯作者Zhang, Xiao-Peng(xiaopeng.zhang@ia.ac.cn)
英文摘要The traditional space-invariant isotropic kernel utilized by a bilateral filter (BF) frequently leads to blurry edges and gradient reversal artifacts due to the existence of a large amount of outliers in the local averaging window. However, the efficient and accurate estimation of space-variant kernels which adapt to image structures, and the fast realization of the corresponding space-variant bilateral filtering are challenging problems. To address these problems, we present a space-variant BF (SVBF), and its linear time and error-bounded acceleration method. First, we accurately estimate spacevariant anisotropic kernels that vary with image structures in linear time through structure tensor and minimum spanning tree. Second, we perform SVBF in linear time using two error-bounded approximation methods, namely, low-rank tensor approximation via higher-order singular value decomposition and exponential sum approximation. Therefore, the proposed SVBF can efficiently achieve good edge-preserving results. We validate the advantages of the proposed filter in applications including: image denoising, image enhancement, and image focus editing. Experimental results demonstrate that our fast and error-bounded SVBF is superior to state-of-the-art methods.
资助项目National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61701235] ; National Natural Science Foundation of China[61772523] ; National Natural Science Foundation of China[61471338] ; National Natural Science Foundation of China[61571046] ; Beijing Natural Science Foundation of China[L182059] ; Fundamental Research Funds for the Central Universities of China[30917011323] ; Open Projects Program of National Laboratory of Pattern Recognition of China[201900020]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000468036000005
出版者SCIENCE PRESS
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities of China ; Open Projects Program of National Laboratory of Pattern Recognition of China
源URL[http://ir.ia.ac.cn/handle/173211/23645]  
专题模式识别国家重点实验室_三维可视计算
通讯作者Zhang, Xiao-Peng
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Nanjing Univ Sci & Technol, Sch Comp Sci & Technol, Nanjing 210094, Jiangsu, Peoples R China
4.Beijing Normal Univ, Sch Geog, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
推荐引用方式
GB/T 7714		 Yuan, Meng-Ke,Dai, Long-Quan,Yan, Dong-Ming,et al. Fast and Error-Bounded Space-Variant Bilateral Filtering[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2019,34(3):550-568.
APA Yuan, Meng-Ke,Dai, Long-Quan,Yan, Dong-Ming,Zhang, Li-Qiang,Xiao, Jun,&Zhang, Xiao-Peng.(2019).Fast and Error-Bounded Space-Variant Bilateral Filtering.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,34(3),550-568.
MLA Yuan, Meng-Ke,et al."Fast and Error-Bounded Space-Variant Bilateral Filtering".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 34.3(2019):550-568.

入库方式： OAI收割

来源：自动化研究所

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