Document Type
Article
Publication Date
9-1-2021
Publication Source
Applied Optics
Abstract
This paper investigates anisoplanatic numerical wave simulation in the context of lucky look imaging. We demonstrate that numerical wave propagation can produce root mean square (RMS) wavefront distributions and probability of lucky look (PLL) statistics that are consistent with Kolmogorov theory. However, the simulated RMS statistics are sensitive to the sampling parameters used in the propagation window. To address this, we propose and validate a new sample spacing rule based on the point source bandwidth used in the propagation and the level of atmospheric turbulence. We use the tuned simulator to parameterize the wavefront RMS probability density function as a function of turbulence strength. The fully parameterized RMS distribution model is used to provide a way to accurately predict the PLL for a range of turbulence strengths. We also propose and validate a new parametric average lucky look optical transfer function (OTF) model that could be used to aid in image restoration. Our OTF model blends the theoretical diffraction-limited OTF and the average turbulence short exposure OTF. Finally, we show simulated images for several anisoplanatic imaging scenarios that reveal the spatially varying nature of the RMS values impacting local image quality.
Inclusive pages
G19-G29
ISBN/ISSN
1559-128X
Copyright
© 2021 Optical Society of America
Publisher
Optical Society of America
Volume
60
Peer Reviewed
yes
Issue
25
Keywords
University of Dayton Electro-optics and Photonics
eCommons Citation
Rucci, Michael A.; Hardie, Russell C.; and Martin, Richard K., "Simulation of anisoplanatic lucky look imaging and statistics through optical turbulence using numerical wave propagation" (2021). Electrical and Computer Engineering Faculty Publications. 421.
https://ecommons.udayton.edu/ece_fac_pub/421
Comments
The document available for download is the authors' accepted manuscript, provided in compliance with the publisher's policy on self-archiving. Permission documentation is on file. To view the version of record, use the DOI: https://doi.org/10.1364/AO.427716