[1] Jiang Wenhan. Progresses on adaptive optics techniques--a review of SPIE's 1991 international symposium on optical applied science and engineering[J]. Opto-Electronic Engineering, 19, 50-60(1992).

[2] Zhao Feifei, Huang Wei, Xu Weicai, et al. Optimization method for the centroid sensing of Shack-Hartmann wavefront sensor[J]. Infrared and Laser Engineering, 43, 3005-3009(2014).

[3] Barchers J D, Fried D L, Link D J. Evaluation of the performance of Hartmann sensors in strong scintillation[J]. Appl Opt, 41, 1012-1021(2002).

[4] Wei Ping, Li Xinyang, Luo Xi, et al. Influence of lack of light in partial subapertures on wavefront reconstruction for Shack-Hartmann wavefront sensor[J]. Chinese Journal of Lasers, 47, 0409002(2020).

[5] Gonsalves R A. Phase retrieval and diversity in adaptive optics[J]. Opt Eng, 21, 829-832(1982).

[6] [6] Gonsalves R A, Chidlaw R. Wavefront sensing by phase retrieval[C]Proc SPIE, Applications of Digital Image Processing III, 1979, 0207: 3239.

[7] Paxman R G, Schulz T J, Fienup J R. Joint estimation of object and aberrations by using phase diversity[J]. Journal of the Optical Society of America A, 9, 1072-1085(1992).

[8] Yang Huizhen, Liu Rong, Liu Qiang. Model wavefront-sensorless adaptive optics system based on eigenmodes of deformable mirror[J]. Infrared and Laser Engineering, 44, 3639-3644(2015).

[9] 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, 351, 300-302(1991).

[10] Barrett T K, Sandler D G. Artificial neural network for the determination of Hubble Space Telescope aberration from stellar images[J]. Appl Opt, 32, 1720-1727(1993).

[11] Nguyen T, Bui V, Lam V, et al. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection[J]. Opt Express, 25, 15043-15057(2017).

[12] Xin Q, Ju G, Zhang C, et al. Object-independent image-based wavefront sensing approach using phase diversity images and deep learning[J]. Opt Express, 27, 26102-26119(2019).

[13] 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).

[14] Tian Qinghua, Lu Chenda, Liu Bo, et al. DNN-based aberration correction in a wavefront sensorless adaptive optics system[J]. Optics Express, 27, 10765-10776(2019).

[15] Yang Nan, Nan Lin, Zhang Dingyi, et al. Research on image interpretation based on deep learning[J]. Infrared and Laser Engineering, 47, 203002(2018).

[16] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[17] [17] He K, Zhang X, Ren S, et al. Deep residual learning f image recognition[C]IEEE Conference on Computer Vision Pattern Recognition (CVPR), 2016.

[18] Chen Tianqi, Li Mu, Li Yutian, et al. MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems[J]. arXiv e-prints, 1512.01274(2015).

[19] Yang Guanci, Yang Jing, Li Shaobo, et al. Modified CNN algorithm based on dropout and ADAM optimizer[J]. Journal of Huazhong University of Science and Technology(Nature Science Edition), 46, 122-127(2018).

[20] Fang Zhiying, Guo Zhengchu, Zhou Dingxuan. Optimal learning rates for distribution regression[J]. Journal of Complexity, 56, 101426(2020).

[21] Voelz D G, Roggemann M C. Digital simulation of scalar optical diffraction: Revisiting chirp function sampling criteria and consequences[J]. Appl Opt, 48, 6132-6142(2009).

[22] Delen N, Hooker B. Free-space beam propagation between arbitrarily oriented planes based on full diffraction theory: A fast Fourier transform approach[J]. Journal of the Optical Society of America A, 15, 857-867(1998).