Wu Ji, Tang Ju, Zhang Mengmeng, Di Jianglei, Hu Liusen, Wu Xiaoyan, Liu Guodong, Zhao Jianlin
Appl Opt. 2022 May 1;61(13):3687-3694. doi: 10.1364/AO.453929.
Adaptive optics (AO) has great applications in many fields and has attracted wide attention from researchers. However, both traditional and deep learning-based AO methods have inherent time delay caused by wavefront sensors and controllers, leading to the inability to truly achieve real-time atmospheric turbulence correction. Hence, future turbulent wavefront prediction plays a particularly important role in AO. Facing the challenge of accurately predicting stochastic turbulence, we combine the convolutional neural network with a turbulence correction time series model and propose a long short-term memory attention-based network, named PredictionNet, to achieve real-time AO correction. Especially, PredictionNet takes the spatiotemporal coupling characteristics of turbulence wavefront into consideration and can improve the accuracy of prediction effectively. The combination of the numerical simulation by a professional software package and the real turbulence experiment by digital holography demonstrates in detail that PredictionNet is more accurate and more stable than traditional methods. Furthermore, the result compared with AO without prediction confirms that predictive AO with PredictionNet is useful.
自适应光学（AO）在许多领域都有广泛应用，并引起了研究人员的广泛关注。然而，传统的和基于深度学习的AO方法都存在由波前传感器和控制器引起的固有时间延迟，导致无法真正实现实时大气湍流校正。因此，未来的湍流波前预测在AO中起着尤为重要的作用。面对准确预测随机湍流的挑战，我们将卷积神经网络与湍流校正时间序列模型相结合，提出了一种基于长短期记忆注意力的网络，名为PredictionNet，以实现实时AO校正。特别是，PredictionNet考虑了湍流波前的时空耦合特性，能够有效提高预测精度。专业软件包的数值模拟与数字全息术的实际湍流实验相结合，详细证明了PredictionNet比传统方法更准确、更稳定。此外，与无预测的AO的比较结果证实了使用PredictionNet的预测性AO是有用的。
