Physics Commons^™

Comparing Multiple Turbulence Restoration Algorithms Performance On Noisy Anisoplanatic Imagery, Michael Armand Rucci, Russell C. Hardie, Alexander J. Dapore

Russell C. Hardie

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 …