Rucci Michael A, Hardie Russell C, Martin Richard K
Appl Opt. 2021 Sep 1;60(25):G19-G29. doi: 10.1364/AO.427716.
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.
本文研究了幸运成像背景下的非等晕数值波模拟。我们证明，数值波传播能够产生与科尔莫戈罗夫理论一致的均方根（RMS）波前分布和幸运成像概率（PLL）统计数据。然而，模拟的RMS统计数据对传播窗口中使用的采样参数很敏感。为了解决这个问题，我们提出并验证了一种基于传播中使用的点源带宽和大气湍流水平的新采样间距规则。我们使用经过调整的模拟器将波前RMS概率密度函数参数化为湍流强度的函数。完全参数化的RMS分布模型用于提供一种准确预测一系列湍流强度下PLL的方法。我们还提出并验证了一种新的参数化平均幸运成像光学传递函数（OTF）模型，该模型可用于辅助图像恢复。我们的OTF模型将理论衍射极限OTF和平均湍流短曝光OTF进行了融合。最后，我们展示了几种非等晕成像场景的模拟图像，这些图像揭示了影响局部图像质量的RMS值的空间变化特性。
