A Unified Learning Framework for Single Image Super-Resolution
文献类型：期刊论文
| 作者 | Yu, Jifei1; Gao, Xinbo1; Tao, Dacheng2,3 ; Li, Xuelong4; Zhang, Kaibing5
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| 刊名 | ieee transactions on neural networks and learning systems
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| 出版日期 | 2014-04-01 |
| 卷号 | 25期号:4页码:780-792 |
| 关键词 | Example learning-based image super-resolution (SR) reconstruction-based self-similarity |
| ISSN号 | 2162-237x |
| 英文摘要 | it has been widely acknowledged that learning- and reconstruction-based super-resolution (sr) methods are effective to generate a high-resolution (hr) image from a single low-resolution (lr) input. however, learning-based methods are prone to introduce unexpected details into resultant hr images. although reconstruction-based methods do not generate obvious artifacts, they tend to blur fine details and end up with unnatural results. in this paper, we propose a new sr framework that seamlessly integrates learning-and reconstruction-based methods for single image sr to: 1) avoid unexpected artifacts introduced by learning-based sr and 2) restore the missing high-frequency details smoothed by reconstruction-based sr. this integrated framework learns a single dictionary from the lr input instead of from external images to hallucinate details, embeds nonlocal means filter in the reconstruction-based sr to enhance edges and suppress artifacts, and gradually magnifies the lr input to the desired high-quality sr result. we demonstrate both visually and quantitatively that the proposed framework produces better results than previous methods from the literature. |
| 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] | support vector regression ; high-resolution image ; quality assessment ; interpolation ; algorithms ; recovery ; limits |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000333098700011 |
| 公开日期 | 2015-03-18 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/22383]  |
| 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.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China 5.Hubei Engn Univ, Sch Comp & Informat Sci, Xiaogan 432000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Jifei,Gao, Xinbo,Tao, Dacheng,et al. A Unified Learning Framework for Single Image Super-Resolution[J]. ieee transactions on neural networks and learning systems,2014,25(4):780-792. |
| APA | Yu, Jifei,Gao, Xinbo,Tao, Dacheng,Li, Xuelong,&Zhang, Kaibing.(2014).A Unified Learning Framework for Single Image Super-Resolution.ieee transactions on neural networks and learning systems,25(4),780-792. |
| MLA | Yu, Jifei,et al."A Unified Learning Framework for Single Image Super-Resolution".ieee transactions on neural networks and learning systems 25.4(2014):780-792. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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