Joint Learning for Single-Image Super-Resolution via a Coupled Constraint
文献类型：期刊论文
| 作者 | Gao, Xinbo1; Zhang, Kaibing1; Tao, Dacheng2; Li, Xuelong3 |
| 刊名 | ieee transactions on image processing
 |
| 出版日期 | 2012-02-01 |
| 卷号 | 21期号:2页码:469-480 |
| 关键词 | Grouping patch pairs (GPPs) joint learning neighbor embedding (NE) super-resolution (SR) |
| ISSN号 | 1057-7149 |
| 产权排序 | 4 |
| 合作状况 | 国际 |
| 英文摘要 | the neighbor-embedding (ne) algorithm for single-image super-resolution (sr) reconstruction assumes that the feature spaces of low-resolution (lr) and high-resolution (hr) patches are locally isometric. however, this is not true for sr because of one-to-many mappings between lr and hr patches. to overcome or at least to reduce the problem for ne-based sr reconstruction, we apply a joint learning technique to train two projection matrices simultaneously and to map the original lr and hr feature spaces onto a unified feature subspace. subsequently, the k-nearest neighbor selection of the input lr image patches is conducted in the unified feature subspace to estimate the reconstruction weights. to handle a large number of samples, joint learning locally exploits a coupled constraint by linking the lr-hr counterparts together with the k-nearest grouping patch pairs. in order to refine further the initial sr estimate, we impose a global reconstruction constraint on the sr outcome based on the maximum a posteriori framework. preliminary experiments suggest that the proposed algorithm outperforms ne-related baselines. |
| WOS标题词 | science & technology ; technology |
| 学科主题 | computer science ; artificial intelligence ; engineering ; electrical & electronic |
| 类目[WOS] | computer science, artificial intelligence ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | high-resolution image ; quality assessment ; motion estimation ; super resolution ; interpolation ; reconstruction ; restoration ; recognition ; algorithm ; sequence |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000300559700004 |
| 公开日期 | 2012-09-03 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/20250]  |
| 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, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Xinbo,Zhang, Kaibing,Tao, Dacheng,et al. Joint Learning for Single-Image Super-Resolution via a Coupled Constraint[J]. ieee transactions on image processing,2012,21(2):469-480. |
| APA | Gao, Xinbo,Zhang, Kaibing,Tao, Dacheng,&Li, Xuelong.(2012).Joint Learning for Single-Image Super-Resolution via a Coupled Constraint.ieee transactions on image processing,21(2),469-480. |
| MLA | Gao, Xinbo,et al."Joint Learning for Single-Image Super-Resolution via a Coupled Constraint".ieee transactions on image processing 21.2(2012):469-480. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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