24 October 2017 One-parameter l1 prior in variational Bayesian super resolution
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Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104622Z (2017) https://doi.org/10.1117/12.2284993
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
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.
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Lei Min, Lei Min, Ping Yang, Ping Yang, Wenjin Liu, Wenjin Liu, Yinsen Luan, Yinsen Luan, Bing Xu, Bing Xu, Yong Liu, Yong Liu, } "One-parameter l1 prior in variational Bayesian super resolution", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104622Z (24 October 2017); doi: 10.1117/12.2284993; https://doi.org/10.1117/12.2284993
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