| Image Super-Resolution With Sparse Neighbor Embedding | |
| Gao, Xinbo1; Zhang, Kaibing1; Tao, Dacheng2,3; Li, Xuelong4 | |
| 作者部门 | 光学影像分析与学习中心 |
| 2012-07-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| ISSN | 1057-7149 |
| 卷号 | 21期号:7页码:3194-3205 |
| 产权排序 | 4 |
| 摘要 | Until now, neighbor-embedding-based (NE) algorithms for super-resolution (SR) have carried out two independent processes to synthesize high-resolution (HR) image patches. In the first process, neighbor search is performed using the Euclidean distance metric, and in the second process, the optimal weights are determined by solving a constrained least squares problem. However, the separate processes are not optimal. In this paper, we propose a sparse neighbor selection scheme for SR reconstruction. We first predetermine a larger number of neighbors as potential candidates and develop an extended Robust-SL0 algorithm to simultaneously find the neighbors and to solve the reconstruction weights. Recognizing that the k-nearest neighbor (k-NN) for reconstruction should have similar local geometric structures based on clustering, we employ a local statistical feature, namely histograms of oriented such clustering. By conveying local structural information of HoG in the synthesis stage, the k-NN of each LR input patch is adaptively chosen from their associated subset, which significantly improves the speed of synthesizing the HR image while preserving the quality of reconstruction. Experimental results suggest that the proposed method can achieve competitive SR quality compared with other state-of-the-art baselines. |
| 文章类型 | Article |
| 关键词 | Histograms Of Oriented Gradients (Hog) Neighbor Embedding (Ne) Sparse Representation Super-resolution (Sr) |
| 学科领域 | Computer Science |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TIP.2012.2190080 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | QUALITY ASSESSMENT ; REPRESENTATION ; INTERPOLATION ; RECONSTRUCTION ; ALGORITHM ; NORM |
| 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:000305577600007 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/20249 |
| 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, Sydney, NSW 2007, Australia 3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 4.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Xinbo,Zhang, Kaibing,Tao, Dacheng,et al. Image Super-Resolution With Sparse Neighbor Embedding[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2012,21(7):3194-3205. |
| APA | Gao, Xinbo,Zhang, Kaibing,Tao, Dacheng,&Li, Xuelong.(2012).Image Super-Resolution With Sparse Neighbor Embedding.IEEE TRANSACTIONS ON IMAGE PROCESSING,21(7),3194-3205. |
| MLA | Gao, Xinbo,et al."Image Super-Resolution With Sparse Neighbor Embedding".IEEE TRANSACTIONS ON IMAGE PROCESSING 21.7(2012):3194-3205. |
| 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 | ||
| Image Super-Resoluti（2328KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
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