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Proceedings Paper

Bayesian multi-frame super-resolution of differently exposed images
Author(s): Jieping Xu; Yonghui Liang; Jin Liu; Zongfu Huang; Xuewen Liu
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Paper Abstract

This paper presents a technique that performs multi-frame super-resolution of differently exposed images. The method first employs a coarse-to-fine image registration method to align image in both spatial and range domain. Then an image fusion method based on the maximum a posterior (MAP) is used to reconstruct a high-resolution image. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs Huber-Markov prior which can reduce the noise and artifacts while reserving image edges. In order to reduce the influence of registration errors, the high-resolution image estimate and registration parameters are refined alternatively by minimizing the cost function. Experiments with synthetic and real images show that the photometric registration reduce the grid-like artifacts in the reconstructed high-resolution image, and the proposed multi-frame super resolution method has a better performance than the interpolation-based method with lower RMSE and less artifacts.

Paper Details

Date Published: 24 October 2017
PDF: 7 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104620B (24 October 2017); doi: 10.1117/12.2281556
Show Author Affiliations
Jieping Xu, National Univ. of Defense Technology (China)
Yonghui Liang, National Univ. of Defense Technology (China)
Jin Liu, National Univ. of Defense Technology (China)
Zongfu Huang, National Univ. of Defense Technology (China)
Xuewen Liu, National Univ. of Defense Technology (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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