The defense, intelligence, and homeland security communities are driving a need for software dominant, real-time or
near-real time atmospheric turbulence compensated imagery. The development of parallel processing capabilities
are finding application in diverse areas including image processing, target tracking, pattern recognition, and image
fusion to name a few. A novel approach to the computationally intensive case of software dominant optical and near
infrared imaging through atmospheric turbulence is addressed in this paper. Previously, the somewhat conventional
wavelength diversity method has been used to compensate for atmospheric turbulence with great success. We apply
a new correlation based approach to the wavelength diversity methodology using a parallel processing architecture
enabling high speed atmospheric turbulence compensation. Methods for optical imaging through distributed
turbulence are discussed, simulation results are presented, and computational and performance assessments are
provided.
Phase 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.
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