Abstract. Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods. Advanced numerical simulators for atmospheric turbulence are available, but they require evaluating wave propagation, which is computationally expensive. We present a propagation-free method for simulating imaging through turbulence with applications in ground-to-ground imaging. The key idea behind our work is a method to draw intermodal and spatially correlated Zernike coefficients. By establishing the equivalence of the angle-of-arrival correlation by Basu, McCrae, and Fiorino (2015) with the multiaperture correlation by Chanan (1992), we show that the Zernike coefficients can be drawn according to a covariance matrix defining the correlations. We propose fast and scalable sampling strategies to draw these samples. The method allows us to compress the wave propagation problem into a sampling problem, hence making the simulator significantly faster than existing ones. Experimental results show that the simulator has an excellent match with the theory and real turbulence data. We anticipate the simulator, which balances speed and accuracy, will be a useful tool for various computational imaging applications.

中文翻译：

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通过对模态间和空间相关的 Zernike 系数进行采样来模拟各向异性湍流

摘要。模拟大气湍流是评估湍流缓解算法和训练基于学习的方法的一项基本任务。大气湍流的高级数值模拟器是可用的，但它们需要评估波的传播，这在计算上是昂贵的。我们提出了一种通过湍流模拟成像的无传播方法，并应用于地对地成像。我们工作背后的关键思想是一种绘制多模态和空间相关泽尼克系数的方法。通过建立 Basu、McCrae 和 Fiorino (2015) 的到达角相关性与 Chanan (1992) 的多孔径相关性的等价性，我们表明可以根据定义相关性的协方差矩阵绘制 Zernike 系数。我们提出了快速且可扩展的采样策略来抽取这些样本。该方法允许我们将波传播问题压缩为采样问题，从而使模拟器比现有模拟器快得多。实验结果表明，该模拟器与理论和实际湍流数据具有很好的匹配性。我们预计平衡速度和准确性的模拟器将成为各种计算成像应用程序的有用工具。