Comparing Multiple Turbulence Restoration Algorithms Performance on Noisy Anisoplanatic ImageryProceedings of SPIE 10204, Long-Range Imaging II
Document TypeConference Paper
AbstractIn 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.
Document VersionPublished Version
CopyrightCopyright © 2017, Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited.
- wave optics,
Citation InformationMichael 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)
Available at: http://works.bepress.com/russell_hardie/63/