Volume 36 Issue 7
May  2024
Turn off MathJax
Article Contents
Qiu Congpan, Liu Guodong, Zhang Dayong, et al. Research progress in deep learning for wavefront reconstruction and wavefront prediction[J]. High Power Laser and Particle Beams, 2024, 36: 071002. doi: 10.11884/HPLPB202436.230430
Citation: Qiu Congpan, Liu Guodong, Zhang Dayong, et al. Research progress in deep learning for wavefront reconstruction and wavefront prediction[J]. High Power Laser and Particle Beams, 2024, 36: 071002. doi: 10.11884/HPLPB202436.230430

Research progress in deep learning for wavefront reconstruction and wavefront prediction

doi: 10.11884/HPLPB202436.230430
  • Received Date: 2023-12-05
  • Accepted Date: 2024-01-31
  • Rev Recd Date: 2024-01-31
  • Available Online: 2024-03-15
  • Publish Date: 2024-05-31
  • The combination of deep learning technology and adaptive optics technology is expected to effectively improve the wavefront correction effect and better cope with more complex environmental conditions. The research progress of applying deep learning in the direction of wavefront reconstruction and wavefront prediction is detailed, including the specific research methods and corresponding neural network structure design adopted by the researchers in these two research directions. The performance of these neural networks in different practical application scenarios is analyzed, the differences between different neural network structures are compared and discussed, and the specific impacts of the structural differences are explored. Finally, the existing methods of deep learning in these two directions are summarized, and the future development trend of the deep integration of deep learning and adaptive optics technology is also prospected.
  • loading
  • [1]
    姜文汉. 自适应光学发展综述[J]. 光电工程, 2018, 45:170489

    Jiang Wenhan. Overview of adaptive optics development[J]. Opto-Electronic Engineering, 2018, 45: 170489
    [2]
    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
    [3]
    Nemoto K, Fujii T, Goto N, et al. Transformation of a laser beam intensity profile by a deformable mirror[J]. Optics Letters, 1996, 21(3): 168-170. doi: 10.1364/OL.21.000168
    [4]
    Amirabadi M A, Kahaei M H, Nezamalhosseini S A. Deep learning based detection technique for FSO communication systems[J]. Physical Communication, 2020, 43: 101229. doi: 10.1016/j.phycom.2020.101229
    [5]
    Senior A W, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning[J]. Nature, 2020, 577(7792): 706-710. doi: 10.1038/s41586-019-1923-7
    [6]
    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
    [7]
    Li Zhaokun, Zhao Xiaohui. BP artificial neural network based wave front correction for sensor-less free space optics communication[J]. Optics Communications, 2017, 385: 219-228. doi: 10.1016/j.optcom.2016.10.037
    [8]
    Jia Peng, Ma Mingyang, Cai Dongmei, 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
    [9]
    Guo Hong, Korablinova N, Ren Qiushi, et al. Wavefront reconstruction with artificial neural networks[J]. Optics Express, 2006, 14(14): 6456-6462. doi: 10.1364/OE.14.006456
    [10]
    Swanson R, Lamb M, Correia C, et al. Wavefront reconstruction and prediction with convolutional neural networks[C]//Proceedings of SPIE 10703, Adaptive Optics Systems VI. 2018: 107031F.
    [11]
    DuBose T B, Gardner D F, Watnik A T. Intensity-enhanced deep network wavefront reconstruction in Shack-Hartmann sensors[J]. Optics Letters, 2020, 45(7): 1699-1702. doi: 10.1364/OL.389895
    [12]
    Hu Shuwen, Hu Lejia, Gong Wei, et al. Deep learning based wavefront sensor for complex wavefront detection in adaptive optical microscopes[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(10): 1277-1288.
    [13]
    Hu Lejia, Hu Shuwen, Gong Wei, et al. Learning-based Shack-Hartmann wavefront sensor for high-order aberration detection[J]. Optics Express, 2019, 27(23): 33504-33517. doi: 10.1364/OE.27.033504
    [14]
    Hu Lejia, Hu Shuwen, Gong Wei, et al. Deep learning assisted Shack-Hartmann wavefront sensor for direct wavefront detection[J]. Optics Letters, 2020, 45(13): 3741-3744. doi: 10.1364/OL.395579
    [15]
    He Yulong, Liu Zhiwei, Ning Yu, et al. Deep learning wavefront sensing method for Shack-Hartmann sensors with sparse sub-apertures[J]. Optics Express, 2021, 29(11): 17669-17682. doi: 10.1364/OE.427261
    [16]
    Guo Youming, Wu Yu, Li Ying, et al. Deep phase retrieval for astronomical Shack–Hartmann wavefront sensors[J]. Monthly Notices of the Royal Astronomical Society, 2022, 510(3): 4347-4354. doi: 10.1093/mnras/stab3690
    [17]
    De Bruijne B, Vdovin G, Soloviev O. Extended scene deep learning wavefront sensing[J]. Journal of the Optical Society of America A, 2022, 39(4): 621-627. doi: 10.1364/JOSAA.443436
    [18]
    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
    [19]
    Jin Yuncheng, Zhang Yiye, Hu Lejia, et al. Machine learning guided rapid focusing with sensor-less aberration corrections[J]. Optics Express, 2018, 26(23): 30162-30171. doi: 10.1364/OE.26.030162
    [20]
    Jin Yuncheng, Chen Jiajia, Wu Chenxue, et al. Wavefront reconstruction based on deep transfer learning for microscopy[J]. Optics Express, 2020, 28(14): 20738-20747. doi: 10.1364/OE.396321
    [21]
    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
    [22]
    Siddik A B, Sandoval S, Voelz D, et al. Deep learning estimation of modified Zernike coefficients and recovery of point spread functions in turbulence[J]. Optics Express, 2023, 31(14): 22903-22913. doi: 10.1364/OE.493229
    [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]
    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
    [25]
    Ju Guohao, Qi Xin, Ma Hongcai, et al. Feature-based phase retrieval wavefront sensing approach using machine learning[J]. Optics Express, 2018, 26(24): 31767-31783. doi: 10.1364/OE.26.031767
    [26]
    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): 26102-23119. doi: 10.1364/OE.27.026102
    [27]
    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
    [28]
    Guo Hongyang, Xu Yangjie, Li Qing, et al. Improved machine learning approach for wavefront sensing[J]. Sensors, 2019, 19: 3533. doi: 10.3390/s19163533
    [29]
    Wu Yu, Guo Youming, Bao Hua, et al. Sub-millisecond phase retrieval for phase-diversity wavefront sensor[J]. Sensors, 2020, 20: 4877. doi: 10.3390/s20174877
    [30]
    Jorgenson M B, Aitken G J M. Prediction of atmospherically induced wave-front degradations[J]. Optics Letters, 1992, 17(7): 466-468. doi: 10.1364/OL.17.000466
    [31]
    Montera D A, Welsh B M, Roggemann M C, et al. Processing wave-front-sensor slope measurements using artificial neural networks[J]. Applied Optics, 1996, 35(21): 4238-4251. doi: 10.1364/AO.35.004238
    [32]
    McGuire P C, Sandler D G, Lloyd-Hart M, et al. Adaptive optics: neural network wavefront sensing, reconstruction, and prediction[C]//Proceedings of the 194th W. E. Heraeus Seminar. 1999: 97-138.
    [33]
    Gallant P J, Aitken G J M. Genetic algorithm design of complexity-controlled time-series predictors[C]//2003 IEEE XIII Workshop on Neural Networks for Signal Processing. 2003: 769-778.
    [34]
    颜召军, 李新阳. 基于神经网络的自适应光学系统变形镜控制电压预测方法[J]. 光学学报, 2010, 30(4):911-916 doi: 10.3788/AOS20103004.0911

    Yan Zhaojun, Li Xinyang. Neural network prediction algorithm for control voltage of deformable mirror in adaptive optical system[J]. Acta Optica Sinica, 2010, 30(4): 911-916 doi: 10.3788/AOS20103004.0911
    [35]
    史晓雨, 冯勇, 陈颖, 等. 自适应光学系统变形镜控制电压预测[J]. 强激光与粒子束, 2012, 24(6):1281-1286 doi: 10.3788/HPLPB20122406.1281

    Shi Xiaoyu, Feng Yong, Chen Ying, et al. Predicting control voltages of deformable mirror in adaptive optical system[J]. High Power Laser and Particle Beams, 2012, 24(6): 1281-1286 doi: 10.3788/HPLPB20122406.1281
    [36]
    史晓雨, 冯勇, 陈颖, 等. 一种基于并行化方法的自适应光学闭环预测控制器[J]. 光学学报, 2012, 32:0801005 doi: 10.3788/AOS201232.0801005

    Shi Xiaoyu, Feng Yong, Chen Ying, et al. A novel predictive controller in the adaptive optics control system based on parallelization method[J]. Acta Optica Sinica, 2012, 32: 0801005 doi: 10.3788/AOS201232.0801005
    [37]
    Sun Zhi, Chen Ying, Li Xinyang, et al. A Bayesian regularized artificial neural network for adaptive optics forecasting[J]. Optics Communications, 2017, 382: 519-527. doi: 10.1016/j.optcom.2016.08.035
    [38]
    Wang Ning, Zhu Licheng, Ma Shuai, et al. Deep learning-based prediction algorithm on atmospheric turbulence-induced wavefront for adaptive optics[J]. IEEE Photonics Journal, 2022, 14(5): 1-10.
    [39]
    Chen Ying. Voltages prediction algorithm based on LSTM recurrent neural network[J]. Optik, 2020, 220: 164869. doi: 10.1016/j.ijleo.2020.164869
    [40]
    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
    [41]
    Liu Xuewen, Morris T, Saunter C, et al. Wavefront prediction using artificial neural networks for open-loop adaptive optics[J]. Monthly Notices of the Royal Astronomical Society, 2020, 496(1): 456-464. doi: 10.1093/mnras/staa1558
    [42]
    Wu Ji, Tang Ju, Zhang Mengmeng, et al. PredictionNet: a long short-term memory-based attention network for atmospheric turbulence prediction in adaptive optics[J]. Applied Optics, 2022, 61(13): 3687-3694. doi: 10.1364/AO.453929
    [43]
    Swanson R, Lamb M, Correia C M, et al. Closed loop predictive control of adaptive optics systems with convolutional neural networks[J]. Monthly Notices of the Royal Astronomical Society, 2021, 503(2): 2944-2954. doi: 10.1093/mnras/stab632
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(11)  / Tables(1)

    Article views (1212) PDF downloads(225) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return