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dc.contributor.authorArrasmith, William W.
dc.contributor.authorSullivan, Sean F.
dc.date.accessioned2017-04-28T19:12:24Z
dc.date.available2017-04-28T19:12:24Z
dc.date.issued2008-04-16
dc.identifier.citationArrasmith, W. W., & Sullivan, S. F. (2008). Parallel implementation of high-speed, phase diverse atmospheric turbulence compensation method on a neural network-based architecture. Paper presented at the Proceedings of SPIE - the International Society for Optical Engineering, 6943 doi:10.1117/12.777687en_US
dc.identifier.urihttp://hdl.handle.net/11141/1380
dc.description.abstractPhase diversity imaging methods work well in removing atmospheric turbulence and some system effects from predominantly near-field imaging systems. However, phase diversity approaches can be computationally intensive and slow. We present a recently adapted, high-speed phase diversity method using a conventional, software-based neural network paradigm. This phase-diversity method has the advantage of eliminating many time consuming, computationally heavy calculations and directly estimates the optical transfer function from the entrance pupil phases or phase differences. Additionally, this method is more accurate than conventional Zernike-based, phase diversity approaches and lends itself to implementation on parallel software or hardware architectures. We use computer simulation to demonstrate how this high-speed, phase diverse imaging method can be implemented on a parallel, highspeed, neural network-based architecture-specifically the Cellular Neural Network (CNN). The CNN architecture was chosen as a representative, neural network-based processing environment because 1) the CNN can be implemented in 2-D or 3-D processing schemes, 2) it can be implemented in hardware or software, 3) recent 2-D implementations of CNN technology have shown a 3 orders of magnitude superiority in speed, area, or power over equivalent digital representations, and 4) a complete development environment exists. We also provide a short discussion on processing speed.en_US
dc.language.isoen_USen_US
dc.rightsThis published article is made available in accordance with publishers policy. It may be subject to U.S. copyright law.en_US
dc.rights.urihttp://spie.org/publications/journals/guidelines-for-authors#Terms_of_Useen_US
dc.titleParallel implementation of high-speed, phase diverse atmospheric turbulence compensation method on a neural network-based architectureen_US
dc.typeConference Proceedingen_US
dc.identifier.doi10.1117/12.777687


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