NDLI: Integrating the Missing Information Estimation into Multi-frame Super-Resolution

Content Provider	Springer Nature Link
Author	Chen, Chuanbo Liang, Hu Zhao, Shengrong Lyu, Zehua Fang, Shaohong Pei, Xiaobing
Copyright Year	2015
Abstract	Multi-frame super-resolution image reconstruction aims to restore a high-resolution image by fusing a set of low-resolution images. The low-resolution images are usually subject to some degradation, such as warping, blurring, down-sampling, or noising, which causes substantial information loss in the low-resolution images, especially in the texture regions. The missing information is not well estimated using existing traditional methods. In this paper, having analyzed the observation model describing the degradation process starting with a high-resolution image and moving to the low-resolution images, we propose a more reasonable observation model that integrates the missing information into the super-resolution reconstruction. Our approach is fully formulated in a Bayesian framework using the Kullback–Leibler divergence. In this way, the missing information is estimated simultaneously with the high-resolution image, motion parameters, and hyper-parameters. Our proposed estimation of the missing information improves the quality of the reconstructed image. Experimental results presented in this paper show improved performance compared with that of existing traditional methods.
Starting Page	1213
Ending Page	1238
Page Count	26
File Format	PDF
ISSN	0278081X
Journal	Circuits, Systems, and Signal Processing
Volume Number	35
Issue Number	4
e-ISSN	15315878
Language	English
Publisher	Springer US
Publisher Date	2015-07-07
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Super-resolution reconstruction Observation model Missing information estimation Total variation model Circuits and Systems Electrical Engineering Signal, Image and Speech Processing Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing

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