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Proceedings Paper

Image restoration for large-motion blurred lunar remote sensing image
Author(s): Zhihai Xu; Pengzhao Ye; Guangmang Cui; Huajun Feng; Qi Li; Yueting Chen
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Paper Abstract

In the field of deep space science detection and high resolution earth observation, a relatively high motion velocity is often generated between the optical camera and the imaging target. Images obtained during the exposure time can produce image motion blur, which becomes one of the main obstacles to acquire high resolution image near the target. As an extended task of the third phase of China’s lunar exploration program, flight imaging of the planned sampling area of Chang’e-5 was carried out. A dual resolution camera with a wide field of view (FOV) camera and a narrow FOV camera was used for imaging mission. High flying speed causes the generation of large motion blurred images captured by the narrow FOV camera and the motion blur can be up to around 30 pixels. To deal with this problem, we analyzed the image features of the blurred images captured by the narrow FOV camera, and proposed a corresponding method that can estimate image motion value from the blurred lunar image based on small craters detection scheme and then adopted the regularization method to restore the image. The algorithm is applied in the batch processing of the real blurred lunar images and has achieved a significant restored effect.

Paper Details

Date Published: 8 March 2017
PDF: 8 pages
Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 1025534 (8 March 2017); doi: 10.1117/12.2266623
Show Author Affiliations
Zhihai Xu, Zhejiang Univ. (China)
Pengzhao Ye, Zhejiang Univ. (China)
Guangmang Cui, Zhejiang Univ. (China)
Huajun Feng, Zhejiang Univ. (China)
Qi Li, Zhejiang Univ. (China)
Yueting Chen, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 10255:
Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016
Yueguang Lv; Jialing Le; Hesheng Chen; Jianyu Wang; Jianda Shao, Editor(s)

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