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Proceedings Paper

Richardson-Lucy deblurring for the star scene under a thinning motion path
Author(s): Laili Su; Xiaopeng Shao; Lin Wang; Haixin Wang; Yining Huang
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Paper Abstract

This paper puts emphasis on how to model and correct image blur that arises from a camera’s ego motion while observing a distant star scene. Concerning the significance of accurate estimation of point spread function (PSF), a new method is employed to obtain blur kernel by thinning star motion path. In particular, how the blurred star image can be corrected to reconstruct the clear scene with a thinning motion blur model which describes the camera’s path is presented. This thinning motion path to build blur kernel model is more effective at modeling the spatially motion blur introduced by camera’s ego motion than conventional blind estimation of kernel-based PSF parameterization. To gain the reconstructed image, firstly, an improved thinning algorithm is used to obtain the star point trajectory, so as to extract the blur kernel of the motion-blurred star image. Then how motion blur model can be incorporated into the Richardson-Lucy (RL) deblurring algorithm, which reveals its overall effectiveness, is detailed. In addition, compared with the conventional estimated blur kernel, experimental results show that the proposed method of using thinning algorithm to get the motion blur kernel is of less complexity, higher efficiency and better accuracy, which contributes to better restoration of the motion-blurred star images.

Paper Details

Date Published: 21 May 2015
PDF: 9 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010L (21 May 2015); doi: 10.1117/12.2176782
Show Author Affiliations
Laili Su, Xidian Univ. (China)
Xiaopeng Shao, Xidian Univ. (China)
Lin Wang, Xidian Univ. (China)
Haixin Wang, Xidian Univ. (China)
Yining Huang, Huawei Technologies Co. Ltd. (China)

Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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