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One-parameter l1 prior in variational Bayesian super resolution
Author(s): Lei Min; Ping Yang; Wenjin Liu; Yinsen Luan; Bing Xu; Yong Liu
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Paper 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.

Paper Details

Date Published: 24 October 2017
PDF: 15 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104622Z (24 October 2017); doi: 10.1117/12.2284993
Show Author Affiliations
Lei Min, Key Lab. on Adaptive Optics (China)
Univ. of Electronic Science and Technology of China (China)
Institute of Optics and Electronics, Chinese Academy of Sciences (China)
Ping Yang, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics, Chinese Academy of Sciences (China)
Wenjin Liu, Key lab. on Adaptive Optics (China)
Institute of Optics and Electronics, Chinese Academy of Sciences (China)
Yinsen Luan, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics, Chinese Academy of Sciences (China)
Bing Xu, Key Lab. on Adaptive Optics (China)
Institute of Optics and Electronics, Chinese Academy of Sciences (China)
Yong Liu, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10462:
AOPC 2017: Optical Sensing and Imaging Technology and Applications
Yadong Jiang; Haimei Gong; Weibiao Chen; Jin Li, Editor(s)

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