## 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.

© 2021 Optical Society of America

Full Article | PDF Article## Corrections

Michael A. Rucci, Russell C. Hardie, and Richard K. Martin, "Simulation of anisoplanatic lucky look imaging and statistics through optical turbulence using numerical wave propagation: erratum," Appl. Opt.**61**, 5734-5734 (2022)

https://opg.optica.org/ao/abstract.cfm?uri=ao-61-19-5734

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