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： O439
- Received：11 January 2023，
  
  Revised：09 February 2023，
  
  Published：05 June 2023
- 稿件说明：
移动端阅览
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, 2023, 38(6): 789-797.
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, 2023, 38(6): 789-797. 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.

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references

ANGEL J R P . Ground-based imaging of extrasolar planets using adaptive optics ［J］. Nature ， 1994 ， 368 （ 6468 ）： 203 - 207 . doi: 10.1038/368203a0 http://dx.doi.org/10.1038/368203a0

HU L F ， XUAN L ， LIU Y J ， et al . Phase-only liquid-crystal spatial light modulator for wavefront correction with high precision ［J］. Optics Express ， 2004 ， 12 （ 26 ）： 6403 - 6409 . doi: 10.1364/opex.12.006403 http://dx.doi.org/10.1364/opex.12.006403

赵子云，顾虎，马文超，等．自适应光学系统误差分析与参数优化研究［J］．液晶与显示， 2021 ， 36 （ 5 ）： 663 - 672 ． doi: 10.37188/CJLCD.2020-0356 http://dx.doi.org/10.37188/CJLCD.2020-0356

ZHAO Z Y ， GU H ， MA W C ， et al . Error budget and parameters optimization of adaptive optics system ［J］. Chinese Journal of Liquid Crystals and Displays ， 2021 ， 36 （ 5 ）： 663 - 672 . （in Chinese） . doi: 10.37188/CJLCD.2020-0356 http://dx.doi.org/10.37188/CJLCD.2020-0356

GU H ， ZHAO Z Y ， ZHANG Z G ， et al . High-precision wavefront reconstruction from Shack-Hartmann wavefront sensor data by a deep convolutional neural network ［J］. Measurement Science and Technology ， 2021 ， 32 （ 8 ）： 085101 . doi: 10.1088/1361-6501/abf708 http://dx.doi.org/10.1088/1361-6501/abf708

DUNCAN J L ， ZHANG Y H ， GANDHI J ， et al . High-resolution imaging with adaptive optics in patients with inherited retinal degeneration ［J］. Investigative Ophthalmology & Visual Science ， 2007 ， 48 （ 7 ）： 3283 - 3291 . doi: 10.1167/iovs.06-1422 http://dx.doi.org/10.1167/iovs.06-1422

POYNEER L A ， GAVEL D T ， BRASE J M . Fast wave-front reconstruction in large adaptive optics systems with use of the Fourier transform ［J］. Journal of the Optical Society of America ， 2002 ， 19 （ 10 ）： 2100 - 2111 . doi: 10.1364/josaa.19.002100 http://dx.doi.org/10.1364/josaa.19.002100

陈浩，宣丽，胡立发，等．望远镜的紧凑型闭环液晶自适应光学系统设计［J］．液晶与显示， 2010 ， 25 （ 3 ）： 379 - 385 ． doi: 10.3969/j.issn.1007-2780.2010.03.017 http://dx.doi.org/10.3969/j.issn.1007-2780.2010.03.017

CHEN H ， XUAN L ， HU L F ， et al . Design on compact type closed-loop liquid crystal adaptive optical system for telescope ［J］. Chinese Journal of Liquid Crystals and Displays ， 2010 ， 25 （ 3 ）： 379 - 385 . （in Chinese） . doi: 10.3969/j.issn.1007-2780.2010.03.017 http://dx.doi.org/10.3969/j.issn.1007-2780.2010.03.017

POLANS J ， MCNABB R P ， IZATT J A ， et al . Compressed wavefront sensing ［J］. Optics Letters ， 2014 ， 39 （ 5 ）： 1189 - 1192 . doi: 10.1364/ol.39.001189 http://dx.doi.org/10.1364/ol.39.001189

HOWLAND G A ， LUM D J ， HOWELL J C . Compressive wavefront sensing with weak value ［J］. Optics Express ， 2014 ， 22 （ 16 ）： 18870 - 18880 . doi: 10.1364/oe.22.018870 http://dx.doi.org/10.1364/oe.22.018870

TIK E C M ， WANG X ， GUO N Q ， et al . Investigation on compressed wavefront sensing in freeform surface measurements ［C］. 2016 4th International Conference on Photonics， Optics and Laser Technology . Rome ： IEEE ， 2016 ： 1 - 6 . doi: 10.5220/0005743901520157 http://dx.doi.org/10.5220/0005743901520157

KE X Z ， WU J L ， HAO J X . Distorted wavefront reconstruction based on compressed sensing ［J］. Applied Physics B ， 2022 ， 128 ： 107 . doi: 10.1007/s00340-022-07827-6 http://dx.doi.org/10.1007/s00340-022-07827-6

NISHIZAKI Y ， VALDIVIA M ， HORISAKI R ， et al . Deep learning wavefront sensing ［J］. Optics Express ， 2019 ， 27 （ 1 ）： 240 - 251 . doi: 10.1364/oe.27.000240 http://dx.doi.org/10.1364/oe.27.000240

GUO H Y ， XU Y J ， LI Q ， et al . Improved machine learning approach for wavefront sensing ［J］. Sensors ， 2019 ， 19 （ 16 ）： 3533 . doi: 10.3390/s19163533 http://dx.doi.org/10.3390/s19163533

VISHNIAKOU I ， SEELIG J D . Wavefront correction for adaptive optics with reflected light and deep neural networks ［J］. Optics Express ， 2020 ， 28 （ 10 ）： 15459 - 15471 . doi: 10.1364/oe.392794 http://dx.doi.org/10.1364/oe.392794

JIA P ， MA M Y ， CAI D M ， et al . Compressive Shack-Hartmann wavefront sensor based on deep neural networks ［J］. Monthly Notices of the Royal Astronomical Society ， 2021 ， 503 （ 3 ）： 3194 - 3203 . doi: 10.1093/mnras/staa4045 http://dx.doi.org/10.1093/mnras/staa4045

SOUTHWELL W H . Wave-front estimation from wave-front slope measurements ［J］. Journal of the Optical Society of America ， 1980 ， 70 （ 8 ）： 998 - 1006 . doi: 10.1364/josa.70.000998 http://dx.doi.org/10.1364/josa.70.000998

FRIED D L . Least-square fitting a wave-front distortion estimate to an array of phase-difference measurements ［J］. Journal of the Optical Society of America ， 1977 ， 67 （ 3 ）： 370 - 375 . doi: 10.1364/josa.67.000370 http://dx.doi.org/10.1364/josa.67.000370

BARANIUK R ， DAVENPORT M ， DEVORE R ， et al . A simple proof of the restricted isometry property for random matrices ［J］. Constructive Approximation ， 2008 ， 28 （ 3 ）： 253 - 263 . doi: 10.1007/s00365-007-9003-x http://dx.doi.org/10.1007/s00365-007-9003-x

CANDÈS E J . The restricted isometry property and its implications for compressed sensing ［J］. Comptes Rendus Mathematique ， 2008 ， 346 （ 9/10 ）： 589 - 592 . doi: 10.1016/j.crma.2008.03.014 http://dx.doi.org/10.1016/j.crma.2008.03.014

DANDES E J ， TAO T . Near-optimal signal recovery from random projections： Universal encoding strategies ［J］. IEEE Transactions on Information Theory ， 2006 ， 52 （ 12 ）： 5406 - 5425 . doi: 10.1109/tit.2006.885507 http://dx.doi.org/10.1109/tit.2006.885507

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