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Photonics Research
Vol. 11, Issue 11, 1802 (2023)

Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics

Ju Tang^1,†, Ji Wu^1,†, Jiawei Zhang¹, Mengmeng Zhang¹..., Zhenbo Ren^1,2,5,*, Jianglei Di^1,3,6,*, Liusen Hu⁴, Guodong Liu⁴ and Jianlin Zhao^1,7,*|Show fewer author(s)

Author Affiliations

¹Key Laboratory of Light Field Manipulation and Information Acquisition, Ministry of Industry and Information Technology, and Shaanxi Key Laboratory of Optical Information Technology, School of Physical Science and Technology, Northwestern Polytechnical University, Xi’an 710129, China

²Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China

³Key Laboratory of Photonic Technology for Integrated Sensing and Communication, Ministry of Education, and Guangdong Provincial Key Laboratory of Information Photonics Technology, Guangdong University of Technology, Guangzhou 510006, China

⁴Institute of Fluid Physics, China Academy of Engineering Physics, Mianyang 621900, China

⁵e-mail: zbren@nwpu.edu.cn

⁶e-mail: jiangleidi@gdut.edu.cn

⁷e-mail: jlzhao@nwpu.edu.cn

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DOI: 10.1364/PRJ.497909 Cite this Article Set citation alerts

Ju Tang, Ji Wu, Jiawei Zhang, Mengmeng Zhang, Zhenbo Ren, Jianglei Di, Liusen Hu, Guodong Liu, Jianlin Zhao, "Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics," Photonics Res. 11, 1802 (2023) Copy Citation Text

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References

[1] D. Kim, H. Choi, T. Brendel, H. Quach, M. Esparza, H. Kang, Y. Feng, J. N. Ashcraft, X. Ke, T. Wang, E. S. Douglas. Advances in optical engineering for future telescopes. Opto-Electron. Adv., 4, 210040(2021).

[2] H. Chang, X. Yin, H. Yao, J. Wang, R. Gao, X. Xin, M. Guizani. Adaptive optics compensation for orbital angular momentum optical wireless communications. IEEE Trans. Wireless Commun., 21, 11151-11163(2022).

[3] C. Rodríguez, A. Chen, J. A. Rivera, M. A. Mohr, Y. Liang, R. G. Natan, W. Sun, D. E. Milkie, T. G. Bifano, X. Chen, N. Ji. An adaptive optics module for deep tissue multiphoton imaging in vivo. Nat. Methods, 18, 1259-1264(2021).

[4] Y. Guo, L. Zhong, L. Min, J. Wang, Y. Wu, K. Chen, K. Wei, C. Rao. Adaptive optics based on machine learning: a review. Opto-Electron. Adv., 5, 200082(2022).

[5] M. A. M. van Kooten, N. Doelman, M. Kenworthy. Robustness of prediction for extreme adaptive optics systems under various observing conditions: an analysis using VLT/sphere adaptive optics data. Astron. Astrophys., 636, A81(2020).

[6] M. Salama, J. Ou, C. Baranec, M. C. Liu, B. P. Bowler, P. Barnes, M. Bonnet, M. Chun, D. A. Duev, S. Goebel. Large adaptive optics survey for substellar objects around young, nearby, low-mass stars with robo-AO. Astron. J., 162, 102(2021).

[7] I. Toselli, S. Gladysz. Improving system performance by using adaptive optics and aperture averaging for laser communications in oceanic turbulence. Opt. Express, 28, 17347-17361(2020).

[8] M. B. Jorgenson, G. J. M. Aitken. Prediction of atmospherically induced wave-front degradations. Opt. Lett., 17, 466-468(1992).

[9] M. Van Kooten, N. Doelman, M. Kenworthy. Impact of time-variant turbulence behavior on prediction for adaptive optics systems. J. Opt. Soc. Am. A, 36, 731-740(2019).

[10] K. Jackson, C. Correia, O. Lardière, D. Andersen, C. Bradley. Linear prediction of atmospheric wave-fronts for tomographic adaptive optics systems: modelling and robustness assessment. Opt. Lett., 40, 143-146(2015).

[11] C. Liu, L. Hu, Z. Cao, Q. Mu, L. Xuan. Modal prediction of atmospheric turbulence wavefront for open-loop liquid-crystal adaptive optics system with recursive least-squares algorithm. Opt. Commun., 285, 238-244(2012).

[12] L. A. Poyneer, B. A. Macintosh, J. P. Véran. Fourier transform wavefront control with adaptive prediction of the atmosphere. J. Opt. Soc. Am. A, 24, 2645-2660(2007).

[13] X. Liu, T. Morris, C. Saunter, F. J. de Cos Juez, C. González-Gutiérrez, L. Bardou. Wavefront prediction using artificial neural networks for open-loop adaptive optics. Mon. Not. R. Astron. Soc., 496, 456-464(2020).

[14] Y. Chen. LSTM recurrent neural network prediction algorithm based on Zernike modal coefficients. Optik, 203, 163796(2020).

[15] Y. Chen. Voltages prediction algorithm based on LSTM recurrent neural network. Optik, 220, 164869(2020).

[16] S. Hochreiter, J. Schmidhuber. Long short-term memory. Neural Comput., 9, 1735-1780(1997).

[17] J. G. Chen, V. Shah, L. Liu. Performance of a U-Net-based neural network for predictive adaptive optics. Opt. Lett., 46, 2513-2516(2021).

[18] X. Shi, Z. Chen, H. Wang, D. Y. Yeung, W. K. Wong, W. C. Woo. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS), 802-810(2015).

[19] R. Swanson, M. Lamb, C. M. Correia, S. Sivanandam, K. Kutulakos. Closed loop predictive control of adaptive optics systems with convolutional neural networks. Mon. Not. R. Astron. Soc., 503, 2944-2954(2021).

[20] J. Wu, J. Tang, M. Zhang, J. Di, L. Hu, X. Wu, G. Liu, J. Zhao. PredictionNet: a long short-term memory-based attention network for atmospheric turbulence prediction in adaptive optics. Appl. Opt., 61, 3687-3694(2022).

[21] K. Wang, M. Zhang, J. Tang, L. Wang, L. Hu, X. Wu, W. Li, J. Di, G. Liu, J. Zhao. Deep learning wavefront sensing and aberration correction in atmospheric turbulence. PhotoniX, 2, 8(2021).

[22] J. Tang, J. Zhang, S. Zhang, S. Mao, Z. Ren, J. Di, J. Zhao. Phase aberration compensation via a self-supervised sparse constraint network in digital holographic microscopy. Opt. Laser Eng., 168, 107671(2023).

[23] C. Lee, G. Song, H. Kim, J. C. Ye, M. Jang. Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data. Nat. Mach. Intell., 5, 35-45(2023).

[24] R. J. Noll. Zernike polynomials and atmospheric turbulence. J. Opt. Soc. Am., 66, 207-211(1976).

[25] G. Dai. Modal wave-front reconstruction with Zernike polynomials and Karhunen–Loève functions. J. Opt. Soc. Am. A, 13, 1218-1225(1996).

[26] Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, C. Zhang. Connecting the dots: multivariate time series forecasting with graph neural networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 753-763(2020).

[27] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, E. Dumitru, V. Vincent, A. Rabinovich. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9(2015).

[28] R. Conan, C. Correia. Object-oriented Matlab adaptive optics toolbox. Proc. SPIE, 9148, 91486C(2014).

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Ju Tang, Ji Wu, Jiawei Zhang, Mengmeng Zhang, Zhenbo Ren, Jianglei Di, Liusen Hu, Guodong Liu, Jianlin Zhao, "Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics," Photonics Res. 11, 1802 (2023)

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