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Proceedings Paper

The improved deconvolution video restoration technology based on the infrared detection system
Author(s): Si-jian Li; Xiang Fan; Bin Zhu; Zheng-dong Cheng
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Paper Abstract

The current infrared detection systems commonly memory and transfer image signals in the form of video. However, when the videos are in the process of formation, transmission and storage, they are easily polluted by motion blur and noise. Accordingly, the video motion blur recovery algorithm was proposed based on this system. Firstly, the video motion blur restoration module was built based on video streaming by integrating mutual information of every frame of sequence images. Secondly, the corresponding algorithm was put forward and the point spread function (PSF) was estimated effectively. Thirdly, the motion blur recovery process was described and all the function module were created. And then, in order to reduce the calculation burden, the image sequence was equal interval sampled from the original video, which enhancing the image quality and achieving better restoration effect. Finally, a subjective and an objective evaluation system were introduced to compare our algorithm with two other classical algorithms and evaluate results. The experimental results show that the peak signal-to-noise ratio of each frame of restored video reached 37, mean square error was below 9, which was superior to the control algorithm. The results basically meet the requirements of detection system, which discovering targets and monitoring the airspace.

Paper Details

Date Published: 24 October 2017
PDF: 10 pages
Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104620A (24 October 2017); doi: 10.1117/12.2281525
Show Author Affiliations
Si-jian Li, Electronic Engineering Institute (China)
Xiang Fan, Electronic Engineering Institute (China)
Bin Zhu, Electronic Engineering Institute (China)
Zheng-dong Cheng, Electronic Engineering Institute (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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