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Kullback–Leibler Divergence Based Composite Prior Modeling for Bayesian Super-Resolution

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Abstract

This paper proposes to adaptively combine the known total variation model and more recent Frobenius norm regularization for multi-frame image super-resolution (SR). In contrast to existing literature, in this paper both the composite prior modeling and posterior variational optimization are achieved in the Bayesian framework by utilizing the Kullback–Leibler divergence, and hyper-parameters related to the composite prior and noise statistics are all determined automatically, resulting in a spatially adaptive SR reconstruction method. Experimental results demonstrate that the new approach can generate a super-resolved image with higher signal-to-noise ratio and better visual perception, not only image details better preserved but also staircase effects better suppressed.

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Acknowledgments

Many thanks are given to the reviewers for their detailed and insightful comments, which have helped to significantly strengthen this manuscript. Wen-Ze Shao is grateful to Professor Yi-Zhong Ma and Dr. Min Wu for their kind supports in the past years. He also shows many thanks to other kind people for helping him through his lost and sad years. The work is supported in part by the Natural Science Foundation of Jiangsu Province (BK20130868), the Natural Science Research Foundation for Jiangsu Universities (13KJB510022), the Talent Introduction Foundation and Natural Science Foundation of Nanjing University of Posts and Telecommunications (NY212014, NY212039), and the Natural Science Foundation of China (61203270, 61071167).

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Authors and Affiliations

  1. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Wen-Ze Shao

  2. School of Mathematics and Statistics, Nanjing Audit University, Nanjing, 211815, China

    Hai-Song Deng

  3. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Zhi-Hui Wei

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  1. Wen-Ze Shao
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  2. Hai-Song Deng
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  3. Zhi-Hui Wei
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Correspondence to Wen-Ze Shao.

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Shao, WZ., Deng, HS. & Wei, ZH. Kullback–Leibler Divergence Based Composite Prior Modeling for Bayesian Super-Resolution. J Sci Comput 60, 60–78 (2014). https://doi.org/10.1007/s10915-013-9784-y

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  • DOI: https://doi.org/10.1007/s10915-013-9784-y

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