| Citation: | Zhang Zhiguang, Yang Huizhen, Liu Jinlong, et al. Research progress in deep learning based WFSless adaptive optics system[J]. High Power Laser and Particle Beams, 2021, 33: 081004. doi: 10.11884/HPLPB202133.210295
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In recent years, Adaptive Optics (AO) system is developing towards miniaturization and low cost. Because of its simple structure and wide application range, wavefront sensorless (WFSless) AO system has become a research hotspot in related fields. Under the condition that the hardware environment is determined, the system control algorithm determines the correction effect and convergence speed of WFSless AO system. The emerging deep learning and artificial neural network have injected new vitality into the control algorithms of WFSless AO system, and further promoted the theoretical and practical development of WFSless AO. On the basis of summarizing the previous control algorithms of WFSless AO system, the applications of convolution neural network (CNN), long-term memory neural network (LSTM) and deep reinforcement learning in WFSless AO system control in recent years are comprehensively introduced, and characteristics of various deep learning models in WFSless AO system are summarized. Applications of WFSless AO system in astronomical observation, microscopy, ophthalmoscopy, laser telecommunication and other fields are outlined.
| [1] |
Shorter R S. Principles of adaptive optics, 3rd edn., by Robert K. Tyson[J]. Contemporary Physics, 2011, 52(5): 501-502.
|
| [2] |
冯麓, 张玉佩, 宋菲君, 等. 夜天文中的自适应光学[J]. 物理, 2018, 47(6):355-366. (Feng Lu, Zhang Yupei, Song Feijun, et al. Adaptive optics for night astronomy[J]. Physics, 2018, 47(6): 355-366 doi: 10.7693/wl20180602
|
| [3] |
Yazdani R, Hajimahmoodzadeh M, Fallah H R. Adaptive phase aberration correction based on imperialist competitive algorithm[J]. Applied Optics, 2014, 53(1): 132-140. doi: 10.1364/AO.53.000132
|
| [4] |
杨慧珍, 陈波, 李新阳, 等. 自适应光学系统随机并行梯度下降控制算法实验研究[J]. 光学学报, 2008, 28(2):205-210. (Yang Huizhen, Chen Bo, Li Xinyang, et al. Experimental demonstration of stochastic parallel gradient descent control algorithm for adaptive optics system[J]. Acta Optica Sinica, 2008, 28(2): 205-210 doi: 10.3321/j.issn:0253-2239.2008.02.001
|
| [5] |
Facomprez A, Beaurepaire E, Débarre D. Accuracy of correction in modal sensorless adaptive optics[J]. Optics Express, 2012, 20(3): 2598-2612. doi: 10.1364/OE.20.002598
|
| [6] |
Huang Linhai, Rao Changhui. Wavefront sensorless adaptive optics: a general model-based approach[J]. Optics Express, 2011, 19(1): 371-379. doi: 10.1364/OE.19.000371
|
| [7] |
方舟, 徐项项, 李鑫, 等. 自适应增益的SPGD算法[J]. 红外与激光工程, 2020, 49:20200274. (Fang Zhou, Xu Xiangxiang, Li Xin, et al. SPGD algorithm with adaptive gain[J]. Infrared and Laser Engineering, 2020, 49: 20200274
|
| [8] |
Zommer S, Ribak E N, Lipson S G, et al. Simulated annealing in ocular adaptive optics[J]. Optics Letters, 2006, 31(7): 939-941. doi: 10.1364/OL.31.000939
|
| [9] |
杨平, 许冰, 姜文汉, 等. 遗传算法在自适应光学系统中的应用[J]. 光学学报, 2007, 27(9):1628-1632. (Yang Ping, Xu Bing, Jiang Wenhan, et al. Study of a genetic algorithm used in an adaptive optical system[J]. Acta Optica Sinica, 2007, 27(9): 1628-1632 doi: 10.3321/j.issn:0253-2239.2007.09.017
|
| [10] |
Yang Huizhen, Li Xinyang. Comparison of several stochastic parallel optimization algorithms for adaptive optics system without a wavefront sensor[J]. Optics & Laser Technology, 2011, 43(3): 630-635.
|
| [11] |
Booth M J. Wavefront sensorless adaptive optics for large aberrations[J]. Optics Letters, 2007, 32(1): 5-7. doi: 10.1364/OL.32.000005
|
| [12] |
杨慧珍, 吴健, 龚成龙. 基于模型的无波前探测自适应光学系统[J]. 光学学报, 2014, 34:0801002. (Yang Huizhen, Wu Jian, Gong Chenglong. Model-based sensorless adaptive optics system[J]. Acta Optica Sinica, 2014, 34: 0801002 doi: 10.3788/AOS201434.0801002
|
| [13] |
Wen Lianghua, Yang Ping, Wang Shuai, et al. A high speed model-based approach for wavefront sensorless adaptive optics systems[J]. Optics & Laser Technology, 2018, 99: 124-132.
|
| [14] |
Huang Linhai. Coherent beam combination using a general model-based method[J]. Chinese Physics Letters, 2014, 31: 094205. doi: 10.1088/0256-307X/31/9/094205
|
| [15] |
Wen Lianghua, Yang Ping, Yang Kangjian, et al. Synchronous model-based approach for wavefront sensorless adaptive optics system[J]. Optics Express, 2017, 25(17): 20584-20597. doi: 10.1364/OE.25.020584
|
| [16] |
Song Hong, Fraanje R, Schitter G, et al. Model-based aberration correction in a closed-loop wavefront-sensor-less adaptive optics system[J]. Optics Express, 2010, 18(23): 24070-24084. doi: 10.1364/OE.18.024070
|
| [17] |
Angel J R P, Wizinowich P, Lloyd-Hart M, et al. Adaptive optics for array telescopes using neural-network techniques[J]. Nature, 1990, 348(6298): 221-224. doi: 10.1038/348221a0
|
| [18] |
Sandler D G, Barrett T K, Palmer D A, et al. Use of a neural network to control an adaptive optics system for an astronomical telescope[J]. Nature, 1991, 351(6324): 300-302. doi: 10.1038/351300a0
|
| [19] |
Barrett T K, Sandler D G. Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images[J]. Applied Optics, 1993, 32(10): 1720-1727. doi: 10.1364/AO.32.001720
|
| [20] |
Paine S W, Fienup J R. Machine learning for improved image-based wavefront sensing[J]. Optics Letters, 2018, 43(6): 1235-1238. doi: 10.1364/OL.43.001235
|
| [21] |
Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2818-2826.
|
| [22] |
Byrd R H, Lu Peihuang, Nocedal J, et al. A limited memory algorithm for bound constrained optimization[J]. SIAM Journal on Scientific Computing, 1995, 16(5): 1190-1208. doi: 10.1137/0916069
|
| [23] |
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
|
| [24] |
Chollet F. Xception: Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1251-1258.
|
| [25] |
Kingma D, Ba J. Adam: A method for stochastic optimization[DB/OL]. arXiv preprint, arXiv: 1412.6980, 2014.
|
| [26] |
Tian Qinghua, Lu Chenda, Liu Bo, et al. DNN-based aberration correction in a wavefront sensorless adaptive optics system[J]. Optics Express, 2019, 27(8): 10765-10776. doi: 10.1364/OE.27.010765
|
| [27] |
Polo A, Haber A, Pereira S F, et al. An innovative and efficient method to control the shape of push-pull membrane deformable mirror[J]. Optics Express, 2012, 20(25): 27922-27932. doi: 10.1364/OE.20.027922
|
| [28] |
Gonsalves R A. Phase retrieval and diversity in adaptive optics[J]. Optical Engineering, 1982, 21: 215829.
|
| [29] |
Ma Huimin, Liu Haiqiu, Qiao Yan, et al. Numerical study of adaptive optics compensation based on convolutional neural networks[J]. Optics Communications, 2019, 433: 283-289. doi: 10.1016/j.optcom.2018.10.036
|
| [30] |
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012: 1097-1105.
|
| [31] |
马慧敏, 焦俊, 乔焰, 等. 一种基于光强图像深度学习的波前复原方法[J]. 激光与光电子学进展, 2020, 57:081103. (Ma Huimin, Jiao Jun, Qiao Yan, et al. Wavefront restoration method based on light intensity image deep learning[J]. Laser & Optoelectronics Progress, 2020, 57: 081103
|
| [32] |
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[DB/OL]. arXiv preprint, arXiv: 1409.1556, 2014.
|
| [33] |
Guo Hongyang, Xu Yangjie, Li Qing, et al. Improved machine learning approach for wavefront sensing[J]. Sensors, 2019, 19(16): 3533. doi: 10.3390/s19163533
|
| [34] |
Wu Yu, Guo Youming, Bao Hua, et al. Sub-millisecond phase retrieval for phase-diversity wavefront sensor[J]. Sensors, 2020, 20(17): 4877. doi: 10.3390/s20174877
|
| [35] |
Nvidia Tensor RT[EB/OL]. (2021-07-09). https://developer.nvidia.com/tensorrt.
|
| [36] |
Vera E, Guzmán F, Weinberger C. Boosting the deep learning wavefront sensor for real-time applications [Invited][J]. Applied Optics, 2021, 60(10): B119-B124. doi: 10.1364/AO.417574
|
| [37] |
Weinberger C, Guzmán F, Vera E. Improved training for the deep learning wavefront sensor[C]//Proceedings Volume 11448, Adaptive Optics Systems VII. 2020, 11448: 114484G.
|
| [38] |
Wang Kaiqiang, Zhang Mengmeng, Tang Ju, et al. Deep learning wavefront sensing and aberration correction in atmospheric turbulence[J]. PhotoniX, 2021, 2: 8. doi: 10.1186/s43074-021-00030-4
|
| [39] |
He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 770-778.
|
| [40] |
Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. doi: 10.1162/neco.1997.9.8.1735
|
| [41] |
Xin Qi, Ju Guohao, Zhang Chunyue, et al. Object-independent image-based wavefront sensing approach using phase diversity images and deep learning[J]. Optics Express, 2019, 27(18): 26102-26119. doi: 10.1364/OE.27.026102
|
| [42] |
Liu Xuewen, Morris T, Saunter C. Using long short-term memory for wavefront prediction in adaptive optics[C]//Proceeding of the 28th International Conference on Artificial Neural Networks. Munich: Springer, 2019: 537-542.
|
| [43] |
Chen Ying. LSTM recurrent neural network prediction algorithm based on Zernike modal coefficients[J]. Optik, 2020, 203: 163796. doi: 10.1016/j.ijleo.2019.163796
|
| [44] |
Swanson R, Lamb M, Correia C, et al. Wavefront reconstruction and prediction with convolutional neural networks[C]//Proceedings Volume 10703, Adaptive Optics Systems VI. 2018, 10703: 107031F.
|
| [45] |
Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529-533. doi: 10.1038/nature14236
|
| [46] |
Hu Ke, Xu Bing, Xu Zhenxing, et al. Self-learning control for wavefront sensorless adaptive optics system through deep reinforcement learning[J]. Optik, 2019, 178: 785-793. doi: 10.1016/j.ijleo.2018.09.160
|
| [47] |
Hu Ke, Xu Zhenxing, Yang Wei, et al. Build the structure of WFSless AO system through deep reinforcement learning[J]. IEEE Photonics Technology Letters, 2018, 30(23): 2033-2036. doi: 10.1109/LPT.2018.2874998
|
| [48] |
Konda V R, Tsitsiklis J N. Actor-critic algorithms[C]//Proceedings of Advances in Neural Information Processing Systems. 2000: 1008-1014.
|
| [49] |
Nousiainen J, Rajani C, Kasper M, et al. Adaptive optics control using model-based reinforcement learning[J]. Optics Express, 2021, 29(10): 15327-15344. doi: 10.1364/OE.420270
|
| [50] |
Cantalloube F, Farley O, Milli J, et al. Wind-driven halo in high-contrast images. I. Analysis of the focal-plane images of SPHERE[J]. Astronomy and Astrophysics, 2020, 638: A98. doi: 10.1051/0004-6361/201937397
|
| [51] |
Landman R, Haffert S Y, Radhakrishnan V M, et al. Self-optimizing adaptive optics control with reinforcement learning[C]//Proceedings Volume 11448, Adaptive Optics Systems VII. 2020, 11448: 1144849.
|
| [52] |
Por E H, Haffert S Y, Radhakrishnan V M, et al. High Contrast Imaging for Python (HCIPy): an open-source adaptive optics and coronagraph simulator[C]//Proceedings Volume 10703, Adaptive Optics Systems VI. 2018, 10703: 1070342.
|
| [53] |
Gendron E, Léna P. Astronomical adaptive optics. II. Experimental results of an optimized modal control[J]. Astronomy and Astrophysics Supplement Series, 1995, 111: 153-167.
|
| [54] |
Conan R, Correia C. Object-oriented Matlab adaptive optics toolbox[C]//Proceedings Volume 9148, Adaptive Optics Systems IV. 2014, 9148: 91486C.
|
| [55] |
Rigaut F, Van Dam M. Simulating astronomical adaptive optics systems using YAO[C]//Third AO4ELT Conference - Adaptive Optics for Extremely Large Telescopes. 2013.
|
| [56] |
Basden A G, Bharmal N A, Jenkins D, et al. The Durham Adaptive Optics Simulation Platform (DASP): Current status[J]. SoftwareX, 2018, 7: 63-69. doi: 10.1016/j.softx.2018.02.005
|
| [57] |
Zhu Licheng, Wen Lianghua, Yang Ping, et al. Aberration correction based on wavefront sensorless adaptive optics in membrane diffractive optical telescope[J]. Optics Communications, 2019, 451: 220-225. doi: 10.1016/j.optcom.2019.06.063
|
| [58] |
Marx V. Microscopy: hello, adaptive optics[J]. Nature Methods, 2017, 14(12): 1133-1136. doi: 10.1038/nmeth.4508
|
| [59] |
Booth M J. Adaptive optical microscopy: the ongoing quest for a perfect image[J]. Light: Science & Applications, 2014, 3: e165.
|
| [60] |
Hussain S A, Kubo T, Hall N, et al. Wavefront-sensorless adaptive optics with a laser-free spinning disk confocal microscope[J]. Journal of Microscopy, 2020.
|
| [61] |
刘立新, 张美玲, 吴兆青, 等. 自适应光学在荧光显微镜中的应用[J]. 激光与光电子学进展, 2020, 57:120001. (Liu Lixin, Zhang Meiling, Wu Zhaoqing, et al. Application of adaptive optics in fluorescence microscope[J]. Laser & Optoelectronics Progress, 2020, 57: 120001
|
| [62] |
Qin Zhongya, He Sicong, Yang Chao, et al. Adaptive optics two-photon microscopy enables near-diffraction-limited and functional retinal imaging in vivo[J]. Light: Science & Applications, 2020, 9: 79.
|
| [63] |
Jian Yifan, Lee Sujin, Ju M J, et al. Lens-based wavefront sensorless adaptive optics swept source OCT[J]. Scientific Reports, 2016, 6: 27620. doi: 10.1038/srep27620
|
| [64] |
牛超君, 于诗杰, 韩香娥. 无波前探测自适应光学对光通信性能影响分析[J]. 激光与光电子学进展, 2015, 52:080102. (Niu Chaojun, Yu Shijie, Han Xiang’e. Analysis about effect of wavefront sensorless adaptive optics on optical communication[J]. Laser & Optoelectronics Progress, 2015, 52: 080102
|
| [65] |
徐晓帆, 陆洲. 星地激光通信可靠性保障技术研究现状[J]. 中国电子科学研究院学报, 2018, 13(6):650-657. (Xu Xiaofan, Lu Zhou. Research status of mitigation techniques to assure the reliability of satellite-to-ground laser communications[J]. Journal of CAEIT, 2018, 13(6): 650-657 doi: 10.3969/j.issn.1673-5692.2018.06.005
|
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Wei Haobo, Dai Wanjun, Wang De’en, et al. Coupling correcting system with double deformable mirrors and double Hartman-Shack sensors[J]. High Power Laser and Particle Beams, 2017, 29: 081003. doi: 10.11884/HPLPB201729.170091
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