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Full-Text Articles in Optics
Comparing Multiple Turbulence Restoration Algorithms Performance On Noisy Anisoplanatic Imagery, Michael Armand Rucci, Russell C. Hardie, Alexander J. Dapore
Comparing Multiple Turbulence Restoration Algorithms Performance On Noisy Anisoplanatic Imagery, Michael Armand Rucci, Russell C. Hardie, Alexander J. Dapore
Electrical and Computer Engineering Faculty Publications
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 …
Image Restoration Of Dispersion-Degraded Images From A Liquid-Crystal Beam Steerer, Ronald J. Broessel, Vince Dominic, Russell C. Hardie
Image Restoration Of Dispersion-Degraded Images From A Liquid-Crystal Beam Steerer, Ronald J. Broessel, Vince Dominic, Russell C. Hardie
Electrical and Computer Engineering Faculty Publications
Liquid-crystal arrays represents one of the first practical technologies capable of steering light by electronic control only. We use such a device to steer the field of view of a broadband imaging sensor. Unfortunately, dispersion degrades the image quality by smearing out details in the image and by introducing multiple diffraction orders (echoes) at the detector plane. We present a method to compensate for these unwanted effects and thus restore the broadband images obtained with the beam steerer. We use the beam-propagation method to find the wavelength-dependent impulse response, from which we determine the appropriate Wiener filter. When training data …