ShanghaiTech University Knowledge Management System
Hybrid Function Sparse Representation Towards Image Super Resolution | |
Bian, Junyi1; Lin, Baojun1,2; Zhang, Ke1 | |
2019 | |
会议录名称 | 18TH INTERNATIONAL CONFERENCE ON COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019 |
ISSN | 16113349 |
卷号 | 11679 LNCS |
页码 | 27-37 |
发表状态 | 已发表 |
DOI | 10.1007/978-3-030-29891-3_3 |
摘要 | Sparse representation with training-based dictionary has been shown successful on super resolution(SR) but still have some limitations. Based on the idea of making the magnification of function curve without losing its fidelity, we proposed a function based dictionary on sparse representation for super resolution, called hybrid function sparse representation (HFSR). The dictionary we designed is directly generated by preset hybrid functions without additional training, which can be scaled to any size as is required due to its scalable property. We mixed approximated Heaviside function (AHF), sine function and DCT function as the dictionary. Multi-scale refinement is then proposed to utilize the scalable property of the dictionary to improve the results. In addition, a reconstruct strategy is adopted to deal with the overlaps. The experiments on ‘Set14’ SR dataset show that our method has an excellent performance particularly with regards to images containing rich details and contexts compared with non-learning based state-of-the art methods. © 2019, Springer Nature Switzerland AG. |
会议地点 | Salerno, Italy |
URL | 查看原文 |
收录类别 | EI ; CPCI-S ; CPCI |
出版者 | Springer Verlag |
EI入藏号 | 20194107502916 |
EI主题词 | Optical resolving power |
EI分类号 | Light/Optics:741.1 |
原始文献类型 | Conference article (CA) |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/89396 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_特聘教授组_林宝军组 |
通讯作者 | Lin, Baojun |
作者单位 | 1.ShanghaiTech University, School of Information Science and Technology, Shanghai; 201210, China 2.Chinese Academy of Sciences, Shanghai Engineering Center for Microsatellites, Shanghai; 201203, China |
第一作者单位 | 信息科学与技术学院 |
通讯作者单位 | 信息科学与技术学院 |
第一作者的第一单位 | 信息科学与技术学院 |
推荐引用方式 GB/T 7714 | Bian, Junyi,Lin, Baojun,Zhang, Ke. Hybrid Function Sparse Representation Towards Image Super Resolution[C]:Springer Verlag,2019:27-37. |
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