• Opto-Electronic Advances
  • Vol. 5, Issue 7, 200082 (2022)
Youming Guo1、2、3, Libo Zhong1、2, Lei Min1、2, Jiaying Wang1、2、3, Yu Wu1、2、3, Kele Chen1、2、3, Kai Wei1、2、3, and Changhui Rao1、2、3、*
Author Affiliations
  • 1The Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China
  • 2The Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China
  • 3University of Chinese Academy of Sciences, Beijing 100049, China
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    DOI: 10.29026/oea.2022.200082 Cite this Article
    Youming Guo, Libo Zhong, Lei Min, Jiaying Wang, Yu Wu, Kele Chen, Kai Wei, Changhui Rao. Adaptive optics based on machine learning: a review[J]. Opto-Electronic Advances, 2022, 5(7): 200082 Copy Citation Text show less
    References

    [4] Kubby JA. AdaptiveOpticsforBiologicalImaging (Taylor & Francis, Boca Raton, America, 2013).

    [7] Bennet F, Thearle O, Roberts L, Smith J, Spollard SJ et al. Free-space quantum communication link with adaptive optics. (AMOS, 2018).

    [8] https://afresearchlab.com/news/afrl-demonstrates-worlds-first-daytime-free-space-quantum-communication-enabled-by-adaptive-optics/

    [43] Correia C, Conan JM, Kulcsár C, Raynaud HF, Petit C. Adapting optimal LQG methods to ELT-sized AO systems. In Proceedingsofthe1stAO4ELTConference 07003 (EDP Sciences, 2010); https://doi.org/10.1051/ao4elt/201007003.

    [64] van Noort M, van der Voort LR, Lfdahl MG. Solar image restoration by use of multi-object multi-frame blind deconvolution. In SolarMHDTheoryandObservations: AHighSpatialResolutionPerspectiveASPConferenceSeries (ASP, 2006); https://ui.adsabs.harvard.edu/abs/2006ASPC..354...55V.

    [74] Gómez SLS, González-Gutiérrez C, Alonso ED, Rodríguez JDS, Rodríguez MLS et al. Improving adaptive optics reconstructions with a deep learning approach. In Proceedingsofthe13thInternationalConferenceonHybridArtificialIntelligenceSystems (Springer, 2018);https://doi.org/10.1007/978-3-319-92639-1_7.

    [86] Lloyd-Hart M, McGuire P. Spatio-temporal prediction for adaptive optics wavefront reconstructors. In Proceedings of the European Southern Observatory Conference on Adaptive Optics (ESO, 1996);http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.35.3278&rep=rep1&type=pdf.

    [93] van Kooten M, Doelman N, Kenworthy M. Performance of AO predictive control in the presence of non-stationary turbulence. In Proceedingsofthe5thAO4ELTConference (Instituto de Astrofisica de Canarias, 2017); https://repository.tudelft.nl/view/tno/uuid%3A4a101b83-7e90-44f8-abaa-b7817cc8b16a.

    [94] Béchet C, Tallon M, Le Louarn M. Very low flux adaptive optics using spatial and temporal priors. In Proceedingsofthe1stAO4ELTConference 03010 (EDP Sciences, 2010); https://doi.org/10.1051/ao4elt/201003010

    [97] Ramos AA. Learning to do multiframe blind deconvolution unsupervisedly. arXiv: 2006.01438 (2020).https://doi.org/10.48550/arXiv.2006.01438

    [99] Zhou SC, Zhang JW, Pan JS, Zuo WM, Xie HZ et al. Spatio-temporal filter adaptive network for video deblurring. In Proceedingsof2019IEEE/CVFInternationalConferenceonComputerVision (IEEE, 2019);https://doi.org/10.1109/ICCV.2019.00257.

    [103] Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I et al. Playing Atari with deep reinforcement learning. arXiv: 1312.5602 (2013). https://doi.org/10.48550/arXiv.1312.5602.

    Youming Guo, Libo Zhong, Lei Min, Jiaying Wang, Yu Wu, Kele Chen, Kai Wei, Changhui Rao. Adaptive optics based on machine learning: a review[J]. Opto-Electronic Advances, 2022, 5(7): 200082
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