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Proceedings Paper

Restoration algorithm of heavy turbulence degraded image for space target based on regularization
Author(s): Liang-liang Wang; Zhi-wei Tao; Ming Li; Xin Gao
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Paper Abstract

Restoration of atmospheric turbulence-degraded image is needed to be solved as soon as possible in the field of astronomical space technology. This paper discusses the issue of regularization during the restoration process, a new restoration method of heavy turbulence-degrade image for space target based on regularization is proposed, in which the anisotropic, nonlinear Step-like and Gussian-like regularization models are adopted according to the properties of turbulence point spread function(PSF)and image. The nonlinear regularization functions are suggested to smooth in the process of estimating the PSF and recover the object image. In order to test the validity of the method, a series of restoration experiments are performed on the heavy turbulence-degraded images for space target and the experiment results show that the method is effective to restore the space object from their heavy turbulence-degraded images. Besides, the definition measures and relative definition measures show that the new method is better than the traditional method for restoration result.

Paper Details

Date Published: 16 August 2011
PDF: 8 pages
Proc. SPIE 8196, International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications, 81960P (16 August 2011); doi: 10.1117/12.899877
Show Author Affiliations
Liang-liang Wang, Beijing Institute of Tracking and Telecommunications Technology (China)
Zhi-wei Tao, China Aerospace Science and Technology Corp. (China)
Ming Li, Beijing Institute of Tracking and Telecommunications Technology (China)
Xin Gao, Beijing Institute of Tracking and Telecommunications Technology (China)


Published in SPIE Proceedings Vol. 8196:
International Symposium on Photoelectronic Detection and Imaging 2011: Space Exploration Technologies and Applications
John C. Zarnecki; Carl A. Nardell; Rong Shu; Jianfeng Yang; Yunhua Zhang, Editor(s)

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