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Proceedings Paper

Gaussian total variation blind restoration of ground-based space object imagery
Author(s): Shiping Guo; Rongzhi Zhang; Rong Xu; Changhai Liu; Jisheng Li
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Paper Abstract

We focus on the restoration of ground-based space object adaptive optics (AO) images distorted by atmospheric turbulence. A total variation (TV) blind AO images restoration method taking advantage of low-order Gaussian derivative operators is presented. Unlike previous definition of the TV regularization term, we propose to define the TV prior by the Gaussian gradient operators instead of the general finite-difference gradient operators. Specifically, in each iterative step of alternating minimization when solving the TV blind deconvolution problem, the first-order Gaussian derivative operator (i.e. gradient magnitude of Gaussian) is used to construct the total variation norm of object image, and the secondorder Gaussian derivative operator (i.e. Laplacian of Gaussian) is used to spatially adjust the regularization parameter. Comparative simulation experiments show that this simple improvement is much practicable for ground-based space object images and can provide more robust performance on both restoration accuracy and convergence property.

Paper Details

Date Published: 7 November 2016
PDF: 9 pages
Proc. SPIE 10141, Selected Papers of the Chinese Society for Optical Engineering Conferences held July 2016, 1014104 (7 November 2016); doi: 10.1117/12.2251735
Show Author Affiliations
Shiping Guo, Xi'an Jiaotong Univ. (China)
Rongzhi Zhang, Xi'an Jiaotong Univ. (China)
Xi'an Satellite Control Ctr. (China)
Rong Xu, Xi'an Satellite Control Ctr. (China)
Changhai Liu, Xi'an Satellite Control Ctr. (China)
Jisheng Li, Xi'an Jiaotong Univ. (China)


Published in SPIE Proceedings Vol. 10141:
Selected Papers of the Chinese Society for Optical Engineering Conferences held July 2016
Yueguang Lv; Weimin Bao; Guangjun Zhang, Editor(s)

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