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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]
2017
会议名称Proceedings of SPIE: AOPC 2017: Optical Sensing and Imaging Technology and Applications
会议录名称0277-786X
卷号10462
页码104622Z
摘要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.
关键词super resolution prior model variational Bayesian Kullback-Leibler distance
语种英语
EI分类号2017-2155
文献类型会议论文
条目标识符http://ir.ioe.ac.cn/handle/181551/9023
专题自适应光学技术研究室(八室)
作者单位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
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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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