中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Coarse-to-Fine Learning for Single-Image Super-Resolution

文献类型:期刊论文

作者Zhang, Kaibing1; Tao, Dacheng2,3; Gao, Xinbo4; Li, Xuelong5; Li, Jie6
刊名ieee transactions on neural networks and learning systems
出版日期2017-05-01
卷号28期号:5页码:1109-1122
ISSN号2162-237x
关键词Correlative neighbor regression (CNR) nonlocal means regularization term self-similarity single-image super-resolution (SR)
通讯作者zhang, kb (reprint author), hubei engn univ, sch comp & informat sci, xiaogan 432000, hubei, peoples r china.
产权排序5
英文摘要

this paper develops a coarse-to-fine framework for single-image super-resolution (sr) reconstruction. the coarse-to-fine approach achieves high-quality sr recovery based on the complementary properties of both example learning- and reconstruction-based algorithms: example learning-based sr approaches are useful for generating plausible details from external exemplars but poor at suppressing aliasing artifacts, while reconstruction-based sr methods are propitious for preserving sharp edges yet fail to generate fine details. in the coarse stage of the method, we use a set of simple yet effective mapping functions, learned via correlative neighbor regression of grouped low-resolution (lr) to high-resolution (hr) dictionary atoms, to synthesize an initial sr estimate with particularly low computational cost. in the fine stage, we devise an effective regularization term that seamlessly integrates the properties of local structural regularity, nonlocal self-similarity, and collaborative representation over relevant atoms in a learned hr dictionary, to further improve the visual quality of the initial sr estimation obtained in the coarse stage. the experimental results indicate that our method outperforms other state-of-the-art methods for producing high-quality images despite that both the initial sr estimation and the followed enhancement are cheap to implement.

学科主题computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic
WOS标题词science & technology ; technology
类目[WOS]computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic
研究领域[WOS]computer science ; engineering
关键词[WOS]sparse representation ; kernel regression ; interpolation ; framework ; algorithm ; reconstruction ; regularization
收录类别SCI ; EI
语种英语
WOS记录号WOS:000401981800008
源URL[http://ir.opt.ac.cn/handle/181661/28982]  
专题西安光学精密机械研究所_光学影像学习与分析中心
作者单位1.Hubei Engn Univ, Sch Comp & Informat Sci, Xiaogan 432000, Hubei, Peoples R China
2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, 81 Broadway St, Ultimo, NSW 2007, Australia
3.Univ Technol Sydney, Fac Engn & Informat Technol, 81 Broadway St, Ultimo, NSW 2007, Australia
4.Xidian Univ, State Key Lab Integrated Serv Networks, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China
5.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
6.Xidian Univ, Video & Image Proc Syst Lab, Sch Elect Engn, Xian 710071, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714
Zhang, Kaibing,Tao, Dacheng,Gao, Xinbo,et al. Coarse-to-Fine Learning for Single-Image Super-Resolution[J]. ieee transactions on neural networks and learning systems,2017,28(5):1109-1122.
APA Zhang, Kaibing,Tao, Dacheng,Gao, Xinbo,Li, Xuelong,&Li, Jie.(2017).Coarse-to-Fine Learning for Single-Image Super-Resolution.ieee transactions on neural networks and learning systems,28(5),1109-1122.
MLA Zhang, Kaibing,et al."Coarse-to-Fine Learning for Single-Image Super-Resolution".ieee transactions on neural networks and learning systems 28.5(2017):1109-1122.

入库方式: OAI收割

来源:西安光学精密机械研究所

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