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Proceedings Paper

Least squares blind deconvolution of air to ground imaging
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Paper Abstract

Multi-frame iterative blind deconvolution algorithms for image enhancement have been widely used for over ten years. Originally developed for enhancing astronomical images from large ground based telescopes, the algorithms were adapted for ground based satellite observations. Most algorithms involve some type of multi-frame iterative Bayesian optimization assuming either Poisson or Gaussian statistics. Many algorithms use an iterative conjugate gradient search technique, however it has been our experience that an algorithm based on Gaussian statistics, combined with projection onto convex sets adaptation leads to a simple algorithm that quickly converges to a result. Recently our thrust has been to transition these algorithms to the airborne imaging problem. We present a number of examples. First, results from observation of low earth orbit satellites with uncompensated data taken at the focal plane of a large telescope. Finally we move to the problem of air-to-ground imaging. Such scene based imaging scenarios require an algorithm that can operate in the presence of anisoplanatic effects. For this case we have developed an algorithm that calculates a position varying point-spread function.

Paper Details

Date Published: 13 October 2005
PDF: 8 pages
Proc. SPIE 5981, Optics in Atmospheric Propagation and Adaptive Systems VIII, 59810I (13 October 2005); doi: 10.1117/12.628051
Show Author Affiliations
David Dayton, Applied Technology Associates (United States)
John Gonglewski, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 5981:
Optics in Atmospheric Propagation and Adaptive Systems VIII
Karin Stein; Anton Kohnle, Editor(s)

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