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Comparing Multiple Turbulence Restoration Algorithms Performance on Noisy Anisoplanatic Imagery
Proceedings of SPIE 10204, Long-Range Imaging II
  • Michael Armand Rucci, Air Force Research Laboratory
  • Russell C. Hardie, University of Dayton
  • Alexander J. Dapore, L-3 Communications Cincinnati Electronics
Document Type
Conference Paper
Publication Date
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.
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Document Version
Published Version

Document is provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file. Paper was presented at SPIE Defense + Security, Anaheim, California, United States, April 9, 2017.

Peer Reviewed
  • Imaging,
  • turbulence,
  • anisoplanatism,
  • Monte-Carlo,
  • wave optics,
  • noise,
  • restoration
Citation Information
Michael Armand Rucci, Russell C. Hardie and Alexander J. Dapore. "Comparing Multiple Turbulence Restoration Algorithms Performance on Noisy Anisoplanatic Imagery" Proceedings of SPIE 10204, Long-Range Imaging II (2017)
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