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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 Publication0277-786X
Volume10462
Pages104622Z
2017
Language英语
AbstractIn 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.
Keywordsuper resolution prior model variational Bayesian Kullback-Leibler distance
Conference NameProceedings of SPIE: AOPC 2017: Optical Sensing and Imaging Technology and Applications
EI Classification Number2017-2155
Document Type会议论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/9023
Collection自适应光学技术研究室(八室)
Affiliation1.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.
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