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Proceedings Paper

Robust multiframe images super resolution
Author(s): Caihui Zong; Hui Zhao; Xiaopeng Xie; Chuang Li
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Paper Abstract

Super-resolution image reconstruction is a process to reconstruct high-resolution images from shifted, low-resolution, degraded observations. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their shortcomings. We propose an alternate approach using 1norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.

Paper Details

Date Published: 24 October 2017
PDF: 8 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104623D (24 October 2017); doi: 10.1117/12.2285139
Show Author Affiliations
Caihui Zong, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Science (China)
Hui Zhao, Xi'an Institute of Optics and Precision Mechanics (China)
Xiaopeng Xie, Xi'an Institute of Optics and Precision Mechanics (China)
Univ. of Chinese Academy of Science (China)
Chuang Li, Xi'an Institute of Optics and Precision Mechanics (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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