Chinese Journal of Lasers, Volume. 50, Issue 11, 1101008(2023)

Research Progress of Laser Adaptive Optics Based on Machine Learning

Tao Cheng1, Sicheng Guo1,2, Ning Wang1,2, Mengmeng Zhao1,2, Shuai Wang1、*, and Ping Yang1、**
Author Affiliations
  • 1Key Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, Sichuan, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
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    References(78)

    [1] Babcock H W. The possibility of compensating astronomical seeing[J]. Publications of the Astronomical Society of the Pacific, 65, 229-236(1953).

    [2] Hardy J W, Lefebvre J E, Koliopoulos C L. Real-time atmospheric compensation[J]. Journal of the Optical Society of America A, 67, 360-369(1977).

    [3] Hardy J W[M]. Adaptive optics for astronomical telescopes(1998).

    [4] Merkle F, Hubin N N. Adaptive optics for the European very large telescope[J]. Proceedings of SPIE, 1542, 283-292(1991).

    [5] Genetron E, Cuby J G, Rigaut F J et al. Come-On-Plus project: an upgrade of the come-on adaptive optics prototype system[J]. Proceedings of SPIE, 1542, 296-307(1991).

    [6] Ren D Q, Zhang T Y, Wang G. An optimized high-performance technique for adaptive optics static aberration correction[J]. Opto-Electronic Engineering, 49, 210319(2022).

    [7] Spreen D E, Hogge C B. Characterizing high-altitude horizontal path optical propagation[J]. Proceedings of SPIE, 2120, 2-9(1994).

    [8] Nemoto K, Fujii T, Goto N et al. Transformation of a laser beam intensity profile by a deformable mirror[J]. Optics Letters, 21, 168-170(1996).

    [9] El-Agmy R, Bulte H, Greenaway A H et al. Adaptive beam profile control using a simulated annealing algorithm[J]. Optics Express, 13, 6085-6091(2005).

    [10] Steiner T D, Merritt P H[M]. Airbome laser advanced technology(1998).

    [11] Greenwood D, Primmerman C A. Adaptive optics research at Lincoln Laboratory[J]. Lincoln Laboratory Journal, 5, 3-24(1992).

    [12] Goodno G D, Komine H, McNaught S J et al. Coherent combination of high-power, zigzag slab lasers[J]. Optics Letters, 31, 1247-1249(2006).

    [13] Liang J, Williams D R, Miller D T. Supernormal vision and high-resolution retinal imaging through adaptive optics[J]. Journal of the Optical Society of America A, 14, 2884-2892(1997).

    [14] Vargas-Martín F, Prieto P M, Artal P. Correction of the aberrations in the human eye with a liquid-crystal spatial light modulator: limits to performance[J]. Journal of the Optical Society of America A, 15, 2552-2562(1998).

    [15] Hecht J[M]. Lasers, death rays, and the long, strange quest for the ultimate weapon(2019).

    [16] Liu Z J, Wang H Y, Xu X J. High energy diode pumped gas laser[J]. Chinese Journal of Lasers, 48, 0401001(2021).

    [17] Zhou P, Su R T, Ma Y X et al. Review of coherent laser beam combining research progress in the past decade[J]. Chinese Journal of Lasers, 48, 0401003(2021).

    [18] Liu Z J, Yang W Q, Han K et al. Research on the design criteria of laser weapons[J]. Chinese Journal of Lasers, 48, 1201001(2021).

    [19] Albertine J R. Recent high-energy laser system tests using the MIRACL/SLBD[J]. Proceedings of SPIE, 1871, 229-239(1993).

    [20] Horkovich J, Pomphrey P. Recent advances in alpha high power chemical laser program[C], 2409(1997).

    [21] McNaught S J, Komine H, Weiss S B et al. 100 kW coherently combined slab MOPAs[C](2009).

    [22] Radovic A, Williams M, Rousseau D et al. Machine learning at the energy and intensity frontiers of particle physics[J]. Nature, 560, 41-48(2018).

    [23] Segler M H S, Preuss M, Waller M P. Planning chemical syntheses with deep neural networks and symbolic AI[J]. Nature, 555, 604-610(2018).

    [24] Senior A W, Evans R, Jumper J et al. Improved protein structure prediction using potentials from deep learning[J]. Nature, 577, 706-710(2020).

    [25] Li X Y, Jiang W H. Zernike modal wavefront reconstruction error of Hartmann sensor on measuring the atmosphere disturbed wavefront[J]. High Power Laser and Particle Beams, 14, 243-249(2002).

    [26] Montera D A, Welsh B M, Roggemann M C et al. Use of artificial neural networks for Hartmann-sensor lenslet centroid estimation[J]. Applied Optics, 35, 5747-5757(1996).

    [27] Li Z Q, Li X Y. Centroid computation for Shack-Hartmann wavefront sensor in extreme situations based on artificial neural networks[J]. Optics Express, 26, 31675-31692(2018).

    [28] Guo H, Korablinova N, Ren Q S et al. Wavefront reconstruction with artificial neural networks[J]. Optics Express, 14, 6456-6462(2006).

    [29] Xu Z Q, Wang S, Zhao M M et al. Wavefront reconstruction of a Shack-Hartmann sensor with insufficient lenslets based on an extreme learning machine[J]. Applied Optics, 59, 4768-4774(2020).

    [30] Swanson R, Lamb M, Correia C et al. Wavefront reconstruction and prediction with convolutional neural networks[J]. Proceedings of SPIE, 10703, 107031F(2018).

    [31] DuBose T B, Gardner D F, Watnik A T. Intensity-enhanced deep network wavefront reconstruction in Shack–Hartmann sensors[J]. Optics Letters, 45, 1699-1702(2020).

    [32] Hu L J, Hu S W, Gong W et al. Learning-based Shack-Hartmann wavefront sensor for high-order aberration detection[J]. Optics Express, 27, 33504-33517(2019).

    [33] Hu L J, Hu S W, Gong W et al. Deep learning assisted Shack-Hartmann wavefront sensor for direct wavefront detection[J]. Optics Letters, 45, 3741-3744(2020).

    [34] He Y L, Liu Z W, Ning Y et al. Deep learning wavefront sensing method for Shack-Hartmann sensors with sparse sub-apertures[J]. Optics Express, 29, 17669-17682(2021).

    [35] 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, 32, 085101(2021).

    [36] Guo Y M, Wu Y, Li Y et al. Deep phase retrieval for astronomical Shack-Hartmann wavefront sensors[J]. Monthly Notices of the Royal Astronomical Society, 510, 4347-4354(2022).

    [37] Zhao M M, Zhao W, Wang S et al. Centroid-predicted deep neural network in Shack-Hartmann sensors[J]. IEEE Photonics Journal, 14, 6804810(2021).

    [38] Li X Y, Jiang W. Effective bandwidth analysis of adaptive optics control system[J]. Acta Optica Sinica, 17, 1697-1702(1997).

    [39] Kulcsár C, Raynaud H F, Petit C et al. Minimum variance prediction and control for adaptive optics[J]. Automatica, 48, 1939-1954(2012).

    [40] Poyneer L, Véran J P. Predictive wavefront control for adaptive optics with arbitrary control loop delays[J]. Journal of the Optical Society of America. A, Optics, Image Science, and Vision, 25, 1486-1496(2008).

    [41] Zhang X J, Li X Y, Zhang H M. Prediction algorithm for atmosphere turbulence with control voltage of deformable mirror[J]. High Power Laser and Particle Beams, 18, 757-760(2006).

    [42] Yan Z J, Li X Y, Rao C. Numerical simulation of a prediction control algorithm for close-loop adaptive optical system[J]. Acta Optica Sinica, 31, 0101003(2011).

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

    [44] Yan Z J, Li X Y. Neural network prediction algorithm for control voltage of deformable mirror in adaptive optical system[J]. Acta Optica Sinica, 30, 911-916(2010).

    [45] Shi X Y, Feng Y, Chen Y et al. Predicting control voltages of deformable mirror in adaptive optical system[J]. High Power Laser and Particle Beams, 24, 1281-1286(2012).

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

    [47] Liu X, Tim M, Chris S et al. Wavefront prediction using artificial neural networks for open-loop adaptive optics[J]. Monthly Notices, 496, 456-464(2020).

    [48] Wu J, Tang J, Zhang M M et al. PredictionNet: a long short-term memory-based attention network for atmospheric turbulence prediction in adaptive optics[J]. Applied Optics, 61, 3687-3694(2022).

    [49] Wang N, Zhu L C, Ma S et al. Deep learning-based prediction algorithm on atmospheric turbulence-induced wavefront for adaptive optics[J]. IEEE Photonics Journal, 14, 8554310(2022).

    [50] Primmerman C A, Price T R, Humphreys R A et al. Atmospheric-compensation experiments in strong-scintillation conditions[J]. Applied Optics, 34, 2081-2088(1995).

    [51] Su C X, Dong L Z, Lai B H et al. Adaptive beam clean-up of high power slab lasers using least-squares wavefront reconstruction algorithm with performance-based filtering[J]. Optics Communications, 490, 126886(2021).

    [52] Wang X L, Wang S H, Piao Z et al. Investigation on influence of laser intensity fluctuation on beam cleanup system based on stochastic parallel gradient descent algorithm[J]. Acta Optica Sinica, 30, 1396-1401(2010).

    [53] Ma S Q, Yang P, Lai B H et al. Slab laser beam cleanup based on efficient stochastic parallel gradient descent algorithm[J]. Chinese Journal of Lasers, 47, 0805001(2020).

    [54] Liu W J, Yuan X H, Zhou Z Y et al. Application of hybrid modal algorithm in wavefront sensorless adaptive optics[J]. Opto-Electronic Engineering, 49, 220020(2022).

    [55] Gerchberg R W, Saxton W O. A practical algorithm for the determination of phase from image and diffraction plane pictures[J]. Optik, 35, 274-279(1972).

    [56] Gonsalves R A. Phase retrieval and diversity in adaptive optics[J]. Optical Engineering, 21, 829-832(1982).

    [57] Kong Q F. Research on wavefront phase inversion method based on single focal plane image[D](2019).

    [58] Ju G H, Qi X, Ma H C et al. Feature-based phase retrieval wavefront sensing approach using machine learning[J]. Optics Express, 26, 31767-31783(2018).

    [59] Ma H M, Liu H Q, Qiao Y et al. Numerical study of adaptive optics compensation based on Convolutional Neural Networks[J]. Optics Communications, 433, 283-289(2019).

    [60] Ma H M, Jiao J, Qiao Y et al. Wavefront restoration method based on light intensity image deep learning[J]. Laser & Optoelectronics Progress, 57, 081103(2020).

    [61] Guo H Y, Xu Y J, Li Q et al. Improved machine learning approach for wavefront sensing[J]. Sensors, 19, 3533(2019).

    [62] Wu Y, Guo Y M, Bao H et al. Sub-millisecond phase retrieval for phase-diversity wavefront sensor[J]. Sensors, 20, 4877(2020).

    [63] Paine S W, Fienup J R. Machine learning for improved image-based wavefront sensing[J]. Optics Letters, 43, 1235-1238(2018).

    [64] Nishizaki Y, Valdivia M, Horisaki R et al. Deep learning wavefront sensing[J]. Optics Express, 27, 240-251(2019).

    [65] Tian Q H, Lu C D, Liu B et al. DNN-based aberration correction in a wavefront sensorless adaptive optics system[J]. Optics Express, 27, 10765-10776(2019).

    [66] Qiu X J, Cheng T, Kong L X et al. A single far-field deep learning adaptive optics system based on four-quadrant discrete phase modulation[J]. Sensors, 20, 5106(2020).

    [67] Wang M H, Yuan X H, Guo W. Single-shot wavefront sensing with deep neural networks for free-space optical communications[J]. Optics Express, 29, 3465-3478(2021).

    [68] Xu Z X, Yang P, Hu K et al. Deep learning control model for adaptive optics systems[J]. Applied Optics, 58, 1998-2009(2019).

    [69] Xu Z X, Yang P, Cheng T et al. Self-learning control model for adaptive optics systems and experimental verification[J]. Chinese Journal of Lasers, 47, 0105001(2020).

    [70] Xu Z X, Yang P, Cheng T et al. Self-learning wavefront control model based on far-field index gradient[J]. Chinese Journal of Lasers, 47, 0405001(2020).

    [71] Hu K, Xu Z X, Yang W et al. Build the structure of WFSless AO system through deep reinforcement learning[J]. IEEE Photonics Technology Letters, 30, 2033-2036(2018).

    [72] Vorontsov M A, Carhart G W, Ricklin J C. Adaptive phase-distortion correction based on parallel gradient-descent optimization[J]. Optics Letters, 22, 907-909(1997).

    [73] Wen L H, Ping Y, Yang K J et al. Synchronous model-based approach for wavefront sensorless adaptive optics system[J]. Optics Express, 25, 20584-20597(2017).

    [74] Landman R, Haffert S Y, Radhakrishnan V M et al. Self-optimizing adaptive optics control with reinforcement learning[J]. Proceedings of SPIE, 11448, 1144849(2020).

    [75] Durech E, Newberry W, Franke J et al. Wavefront sensor-less adaptive optics using deep reinforcement learning[J]. Biomedical Optics Express, 12, 5423-5438(2021).

    [76] Jalo N, Chang R, Markus K et al. Adaptive optics control using model-based reinforcement learning[J]. Optics Express, 29, 15327-15344(2021).

    [78] Pou B, Ferreira F, Quinones E et al. Adaptive optics control with multi-agent model-free reinforcement learning[J]. Optics Express, 30, 2991-3015(2022).

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    Tao Cheng, Sicheng Guo, Ning Wang, Mengmeng Zhao, Shuai Wang, Ping Yang. Research Progress of Laser Adaptive Optics Based on Machine Learning[J]. Chinese Journal of Lasers, 2023, 50(11): 1101008

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    Paper Information

    Category: laser devices and laser physics

    Received: Feb. 14, 2023

    Accepted: Apr. 18, 2023

    Published Online: May. 29, 2023

    The Author Email: Wang Shuai (pingyang2516@163.com), Yang Ping (wangshuai@ioe.ac.cn)

    DOI:10.3788/CJL230522

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