Compressed wavefront sensing based on deep neural network for atmospheric turbulence

HUA Sheng-xiao; HU Qi-li; FENG Jia-hao; JIANG Lü; YANG Yan-yan; WU Jing-jing; YU Lin; HU Li-fa

doi:10.37188/CJLCD.2023-0011

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Compressed wavefront sensing based on deep neural network for atmospheric turbulence

Intelligent Imaging | 更新时间：2023-06-12

- Compressed wavefront sensing based on deep neural network for atmospheric turbulence
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 6, Pages: 789-797(2023)
- 作者机构：
  
  1.江南大学理学院，江苏无锡 214122
  2.光电对抗测试与评估技术重点实验室，河南洛阳 471003
  3.江苏省轻工光电工程技术研究中心，江苏无锡 214122
- 作者简介：
- 基金信息：
  
  Fund for Key Laboratory of Electro-Optical Countermeasures Test & Evaluation Technology(GKCP2021001)
- DOI：10.37188/CJLCD.2023-0011
  CLC：
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HUA Sheng-xiao, HU Qi-li, FENG Jia-hao, et al. Compressed wavefront sensing based on deep neural network for atmospheric turbulence. [J]. Chinese Journal of Liquid Crystals and Displays 38(6):789-797(2023)
DOI：

HUA Sheng-xiao, HU Qi-li, FENG Jia-hao, et al. Compressed wavefront sensing based on deep neural network for atmospheric turbulence. [J]. Chinese Journal of Liquid Crystals and Displays 38(6):789-797(2023) DOI： 10.37188/CJLCD.2023-0011.

摘要

压缩感知技术用于光学波前测量时，常规的斜率恢复方法精度较低，难以测量大气湍流引起的复杂波前，本文利用深度神经网络进行斜率恢复，提高斜率恢复精度，从而提高压缩波前探测方法测量大气湍流波前的精度。传统的压缩波前探测方法在稀疏化过程中忽略相对较小的斜率值，导致波前测量误差的增加。为了快速测量大气湍流引起的复杂波前，本文提出了一种深度神经网络，可以高精度地恢复斜率，从而提高了波前重构的精度。在压缩比为0.1~0.9情况下，基于深度神经网络的压缩波前探测算法（DNNCWS）的波前重构误差PV优于0.014 μm，算法的运行时间为4.4 ms。在暗弱星等情况下，残差波前的峰谷值（PV）优于0.011 μm。模拟结果表明，DNNCWS具有良好的抗噪声性能。深度神经网络DNNCWS提高了压缩波前的探测精度，可以用于测量大气湍流引起的复杂像差，还可用于其他自适应光学应用，如激光通信和视网膜成像。

Abstract

When the compressive sensoring is used in wavefront measurement， classic methods of slopes’ restoration has a relatively low precision， which make it difficult to measure the atmospheric turbulence wavefront. In the paper， a deep neural network is presented to improve the slopes’ restoration precision. The traditional compressive sensing technology does not take into account the relatively small slopes， which increases the wavefront measurement errors. To measure the complex wavefront induced by atmospheric turbulence with a high speed， the paper presents an improved deep neural network to restore the slopes from sparse ones with high precision， which improves the precision of wavefront reconstruction. When the compression ratio is ranged from 0.1 to 0.9， the wavefront error PV （Peak to valley） of the compressed wavefront detection algorithm based on depth neural network （DNNCWS） proposed in this paper is better than 0.014 μm， and the running time of the algorithm is 4.4 ms. In the case of low signal-to-noise ratio， the residual wavefront PV is better than 0.011 μm. In addition， the simulation results indicate that it has good anti-noise performance. The DNNCWS improves the detection accuracy of compressive sensing and overcomes the problem of low accuracy for complex aberration induced by atmospheric turbulence. It can also be used in other adaptive optical applications， such as laser communication and retinal imaging.

关键词

压缩波前探测自适应光学大气湍流

Keywords

compressed wavefront sensingadaptive opticsatmospheric turbulence

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