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Proceedings Paper

Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery
Author(s): Michael A. Rucci; Russell C. Hardie; Alexander J. Dapore
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Paper Abstract

In this paper, we compare the performance of multiple turbulence mitigation algorithms to restore imagery degraded by atmospheric turbulence and camera noise. In order to quantify and compare algorithm performance, imaging scenes were simulated by applying noise and varying levels of turbulence. For the simulation, a Monte-Carlo wave optics approach is used to simulate the spatially and temporally varying turbulence in an image sequence. A Poisson-Gaussian noise mixture model is then used to add noise to the observed turbulence image set. These degraded image sets are processed with three separate restoration algorithms: Lucky Look imaging, bispectral speckle imaging, and a block matching method with restoration filter. These algorithms were chosen because they incorporate different approaches and processing techniques. The results quantitatively show how well the algorithms are able to restore the simulated degraded imagery.

Paper Details

Date Published: 1 May 2017
PDF: 6 pages
Proc. SPIE 10204, Long-Range Imaging II, 1020409 (1 May 2017); doi: 10.1117/12.2269133
Show Author Affiliations
Michael A. Rucci, Air Force Research Lab. (United States)
Russell C. Hardie, Univ. of Dayton (United States)
Alexander J. Dapore, L3 Technologies Cincinnati Electronics (United States)


Published in SPIE Proceedings Vol. 10204:
Long-Range Imaging II
Eric J. Kelmelis, Editor(s)

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