| One-parameter l1 Prior in Variational Bayesian Super resolution | |
| Min Lei [1,2,3,4], Yang Ping [1,3], Liu Wenjin [1,3], Luan Yinsen [1,3], Xu Bing [1,3], Liu Yong [2] | |
| Source Publication | 0277-786X |
| Volume | 10462 |
| Pages | 104622Z |
| 2017 | |
| Language | 英语 |
| Abstract | In this paper, we address the multiframe super resolution problem from a set of degraded, under-sampled, shifted and rotated low resolution images to obtain a high resolution image using the variational Bayesian methods. In the Bayesian framework a prior model on the high resolution image need to be specified, its aim is to summarize our knowledge of the image and to constraint the ill-posed image reconstruction problem. Appropriate prior model selection according to the super resolution scenario is a critical issue. Here we propose the one-parameter l1 prior. Experimental results demonstrate that the proposed method is very effective and compared favorably to state-of-the-art super resolution algorithms. |
| Keyword | super resolution prior model variational Bayesian Kullback-Leibler distance |
| Conference Name | Proceedings of SPIE: AOPC 2017: Optical Sensing and Imaging Technology and Applications |
| EI Classification Number | 2017-2155 |
| Document Type | 会议论文 |
| Identifier | http://ir.ioe.ac.cn/handle/181551/9023 |
| Collection | 自适应光学技术研究室（八室） |
| Affiliation | 1.Key Laboratory on Adaptive Optics, Chinese Academy of Sciences , Chengdu, China 610209 2.School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China 3.The Institute of Optics and Electronics the Chinese Academy of Sciences, Chengdu 610209, China 4.University of Chinese Academy of Sciences, Beijing 100039, China |
| Recommended Citation GB/T 7714 | Min Lei [1,2,3,4], Yang Ping [1,3], Liu Wenjin [1,3], Luan Yinsen [1,3], Xu Bing [1,3], Liu Yong [2]. One-parameter l1 Prior in Variational Bayesian Super resolution[C],2017:104622Z. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 2017-2155.pdf（1666KB） | 会议论文 | 开放获取 | CC BY-NC-SA | Application Full Text | ||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment