Parallel implementation of high-speed, phase diverse atmospheric turbulence compensation method on a neural network-based architecture

William W. Arrasmith; Sean F. Sullivan

doi:10.1117/12.777687

16 April 2008 Parallel implementation of high-speed, phase diverse atmospheric turbulence compensation method on a neural network-based architecture

William W. Arrasmith, Sean F. Sullivan

Author Affiliations +

Proceedings Volume 6943, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VII; 69431D (2008) https://doi.org/10.1117/12.777687
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

William W. Arrasmith and Sean F. Sullivan "Parallel implementation of high-speed, phase diverse atmospheric turbulence compensation method on a neural network-based architecture", Proc. SPIE 6943, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VII, 69431D (16 April 2008); https://doi.org/10.1117/12.777687

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Atmospheric turbulence

Imaging systems

Optical transfer functions

Neural networks

Network architectures

Computer architecture

Image processing

Show All Keywords

Keywords/Phrases

Search In:

Publication Years