| Joint Learning for Single-Image Super-Resolution via a Coupled Constraint | |
| Gao, Xinbo1; Zhang, Kaibing1; Tao, Dacheng2; Li, Xuelong3 | |
| 作者部门 | 光学影像分析与学习中心 |
| 2012-02-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| ISSN | 1057-7149 |
| 卷号 | 21期号:2页码:469-480 |
| 产权排序 | 4 |
| 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. |
| 文章类型 | Article |
| 关键词 | Grouping Patch Pairs (Gpps) Joint Learning Neighbor Embedding (Ne) Super-resolution (Sr) |
| 学科领域 | Computer Science |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TIP.2011.2161482 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | HIGH-RESOLUTION IMAGE ; QUALITY ASSESSMENT ; MOTION ESTIMATION ; SUPER RESOLUTION ; INTERPOLATION ; RECONSTRUCTION ; RESTORATION ; RECOGNITION ; ALGORITHM ; SEQUENCE |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000300559700004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/20250 |
| 专题 | 光谱成像技术研究室 |
| 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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Joint Learning for S（1319KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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