湍流
补偿(心理学)
光学
计算机科学
高斯分布
卷积神经网络
人工神经网络
相(物质)
卷积(计算机科学)
物理
人工智能
机械
心理学
量子力学
精神分析
作者
Jingjing Meng,Jianguo He,Min Huang,Yang Li,Baoyu Zhu,Xinxin Kong,Zhe Han,Xin Li,Yang Liu
出处
期刊:Optics Letters
[The Optical Society]
日期:2022-11-21
卷期号:47 (24): 6417-6417
被引量:5
摘要
We propose a deep learning method that includes convolution neural network (CNN) and convolutional long short-term memory (ConvLSTM) models to realize atmospheric turbulence compensation and correction of distorted beams. The trained CNN model can automatically obtain the equivalent turbulent compensation phase screen based on the Gaussian beams affected by turbulence and without turbulence. To solve the time delay problem, we use the ConvLSTM model to predict the atmospheric turbulence evolution and acquire a more accurate compensation phase under the Taylor frozen hypothesis. The experimental results show that the distorted Gaussian and vortex beams are effectively and accurately compensated. © 2020 Optica Publishing Group
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