Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery

Michael A. Rucci; Russell C. Hardie; Alexander J. Dapore

doi:10.1117/12.2269133

1 May 2017 Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery

Michael A. Rucci, Russell C. Hardie, Alexander J. Dapore

Proceedings Volume 10204, Long-Range Imaging II; 1020409 (2017) https://doi.org/10.1117/12.2269133
Event: SPIE Defense + Security, 2017, Anaheim, CA, United States

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.

Citation Download Citation

Michael A. Rucci, Russell C. Hardie, and Alexander J. Dapore "Comparing multiple turbulence restoration algorithms performance on noisy anisoplanatic imagery", Proc. SPIE 10204, Long-Range Imaging II, 1020409 (1 May 2017); https://doi.org/10.1117/12.2269133

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