[1] Angel J R P, Wizinowich P, Lloyd-Hart M et al. Adaptive optics for array telescopes using neural-network techniques[J]. Nature, 348, 221-224(1990).
[2] Jin Y C, Zhang Y Y, Hu L J et al. Machine learning guided rapid focusing with sensor-less aberration corrections[J]. Optics Express, 26, 30162-30171(2018).
[3] Alvarez Diez C, Shao F, Bille J. Pyramid and Hartmann-Shack wavefront sensor with artificial neural network for adaptive optics[J]. Journal of Modern Optics, 55, 683-689(2008).
[4] Guo H, Korablinova N, Ren Q S et al. Wavefront reconstruction with artificial neural networks[J]. Optics Express, 14, 6456-6462(2006).
[5] Wang N, Zhu L C, Ma S et al. Research on distortion wavefront prediction technique for laser communication system based on LSTM network[J]. Proceedings of SPIE, 12169, 121693D(2022).
[6] Liu F W, Wu Y Q, Chen Q et al. Overview of advanced manufacturing technology of large-aperture aspheric mirror[J]. Opto-Electronic Engineering, 47, 65-87(2020).
[7] Jiang W H, Xian H, Shen F. Detecting error of shack-hartmann wavefront sensor[J]. Chinese Journal of Quantum Electronics, 15, 218-227(1998).
[8] Ragazzoni R, Ghedina A, Baruffolo A et al. Testing the pyramid wavefront sensor on the sky[J]. Proceedings of SPIE, 4007, 423-430(2000).
[9] Ghigo M, Diolaiti E, Perennes F et al. Use of the LIGA process for the production of pyramid wavefront sensors for adaptive optics in astronomy[J]. Proceedings of SPIE, 5169, 55-61(2003).
[10] Landman R, Haffert S Y. Nonlinear wavefront reconstruction with convolutional neural networks for Fourier-based wavefront sensors[J]. Optics Express, 28, 16644-16657(2020).
[11] Rosenblatt F[M]. The perceptron, a perceiving and recognizing automaton project para(1957).
[12] Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain[J]. Psychological Review, 65, 386-408(1958).
[13] Olazaran M. A sociological study of the official history of the perceptrons controversy[J]. Social Studies of Science, 26, 611-659(1996).
[14] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).
[15] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database[C], 248-255(2009).
[16] Silver D, Huang A, Maddison C J et al. Mastering the game of go with deep neural networks and tree search[J]. Nature, 529, 484-489(2016).
[17] Silver D, Schrittwieser J, Simonyan K et al. Mastering the game of go without human knowledge[J]. Nature, 550, 354-359(2017).
[18] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).
[19] Huang J T, Li J Y, Gong Y F. An analysis of convolutional neural networks for speech recognition[C], 4989-4993(2015).
[20] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[21] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).
[22] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).
[23] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 9, 1735-1780(1997).
[24] Gers F A, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM[J]. Neural Computation, 12, 2451-2471(2000).
[25] Greff K, Srivastava R K, Koutník J et al. LSTM: a search space odyssey[J]. IEEE Transactions on Neural Networks and Learning Systems, 28, 2222-2232(2017).
[26] Vorontsov M A, Carhart G W, Cohen M et al. Adaptive optics based on analog parallel stochastic optimization: analysis and experimental demonstration[J]. Journal of the Optical Society of America A, 17, 1440-1453(2000).
[27] Weyrauch T, Vorontsov M A. Atmospheric compensation with a speckle beacon in strong scintillation conditions: directed energy and laser communication applications[J]. Applied Optics, 44, 6388-6401(2005).
[28] Zommer S, Ribak E N, Lipson S G et al. Simulated annealing in ocular adaptive optics[J]. Optics Letters, 31, 939-941(2006).
[29] Yang P, Ao M W, Liu Y et al. Intracavity transverse modes controlled by a genetic algorithm based on Zernike mode coefficients[J]. Optics Express, 15, 17051-17062(2007).
[30] Yin X L, Chang H A, Cui X Z et al. Adaptive turbulence compensation with a hybrid input-output algorithm in orbital angular momentum-based free-space optical communication[J]. Applied Optics, 57, 7644-7650(2018).
[31] Chang H, Yin X L, Cui X Z et al. Adaptive optics compensation of orbital angular momentum beams with a modified Gerchberg-Saxton-based phase retrieval algorithm[J]. Optics Communications, 405, 271-275(2017).
[32] Li M, Li Y P, Han J W. Gerchberg-Saxton algorithm based phase correction in optical wireless communication[J]. Physical Communication, 25, 323-327(2017).
[33] 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).
[34] 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).
[35] Dean B H, Aronstein D L, Smith J S et al. Phase retrieval algorithm for JWST Flight and Testbed Telescope[J]. Proceedings of SPIE, 6265, 626511(2006).
[36] Paine S W, Fienup J R. Machine learning for improved image-based wavefront sensing[J]. Optics Letters, 43, 1235-1238(2018).
[37] Byrd R H, Lu P H, Nocedal J et al. A limited memory algorithm for bound constrained optimization[J]. SIAM Journal on Scientific Computing, 16, 1190-1208(1995).
[38] Wang M H, Guo W, Yuan X H. Single-shot wavefront sensing with deep neural networks for free-space optical communications[J]. Optics Express, 29, 3465-3478(2021).
[39] Tan M X, Le Q V. Efficientnet: rethinking model scaling for convolutional neural networks[C], 6105-6114(2019).
[40] Nishizaki Y, Valdivia M, Horisaki R et al. Deep learning wavefront sensing[J]. Optics Express, 27, 240-251(2019).
[41] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).
[42] Jin Y C, Chen J J, Wu C X et al. Wavefront reconstruction based on deep transfer learning for microscopy[J]. Optics Express, 28, 20738-20747(2020).
[43] Sun B C, Saenko K. Deep CORAL: correlation alignment for deep domain adaptation[M]. Hua G, Jégou H. Computer vision-ECCV 2016 workshops. Lecture notes in computer science, 9915, 443-450(2016).
[44] Andersen T, Owner-Petersen M, Enmark A. Neural networks for image-based wavefront sensing for astronomy[J]. Optics Letters, 44, 4618-4621(2019).
[45] Wu Y, Guo Y M, Bao H A et al. Sub-millisecond phase retrieval for phase-diversity wavefront sensor[J]. Sensors, 20, 4877(2020).
[46] LeCun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998).
[47] 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).
[48] Mukundan R, Ong S H, Lee P A. Image analysis by Tchebichef moments[J]. IEEE Transactions on Image Processing, 10, 1357-1364(2001).
[49] Xin Q, Ju G H, Zhang C Y et al. Object-independent image-based wavefront sensing approach using phase diversity images and deep learning[J]. Optics Express, 27, 26102-26119(2019).
[50] Lu C D, Tian Q H, Zhu L et al. Mitigating the ambiguity problem in the CNN-based wavefront correction[J]. Optics Letters, 47, 3251-3254(2022).
[51] Shi Y P, Liu C L. Positioning accuracy improvement of spot centroid for shack-hartmann wavefront sensor[J]. Laser & Optoelectronics Progress, 54, 081201(2017).
[52] 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).
[53] 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).
[54] Swanson R, Lamb M, Correia C et al. Wavefront reconstruction and prediction with convolutional neural networks[J]. Proceedings of SPIE, 10703, 107031F(2018).
[55] Barchers J D, Fried D L, Link D J. Evaluation of the performance of Hartmann sensors in strong scintillation[J]. Applied Optics, 41, 1012-1021(2002).
[56] Akondi V, Steven S, Dubra A. Centroid error due to non-uniform lenslet illumination in the Shack-Hartmann wavefront sensor[J]. Optics Letters, 44, 4167-4170(2019).
[57] 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).
[58] Huang G, Liu Z, Van Der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).
[59] 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).
[60] Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep residual U-net[J]. IEEE Geoscience and Remote Sensing Letters, 15, 749-753(2018).
[62] Dai G M[M]. Wavefront optics for vision correction(2008).
[63] Hu S W, Hu L J, Gong W et al. Deep learning based wavefront sensor for complex wavefront detection in adaptive optical microscopes[J]. Frontiers of Information Technology & Electronic Engineering, 22, 1277-1288(2021).
[64] de Bruijne B, Vdovin G, Soloviev O. Extended scene deep learning wavefront sensing[J]. Journal of the Optical Society of America A, 39, 621-627(2022).
[65] Ragazzoni R, Farinato J. Sensitivity of a pyramidic Wave Front sensor in closed loop adaptive optics[J]. Astronomy and Astrophysics, 350, L23-L26(1999).
[66] Vérinaud C. On the nature of the measurements provided by a pyramid wave-front sensor[J]. Optics Communications, 233, 27-38(2004).
[67] Chen H, Wei L, He Y et al. Deep learning assisted plenoptic wavefront sensor for direct wavefront detection[J]. Optics Express, 31, 2989-3004(2023).
[68] Jiang P Z, Xu J P, Liang Y H et al. Comparison of the Shack-Hartmann and plenoptic sensor in closed-loop adaptive optics system[J]. Optical Engineering, 55, 033105(2016).
[69] Clare R M, Lane R G. Phase retrieval from subdivision of the focal plane with a lenslet array[J]. Applied Optics, 43, 4080-4087(2004).
[70] Clare R M, Lane R G. Wave-front sensing from subdivision of the focal plane with a lenslet array[J]. Journal of the Optical Society of America A, 22, 117-125(2005).
[71] Li Z Q, Li X Y, Liang R G. Random two-frame interferometry based on deep learning[J]. Optics Express, 28, 24747-24760(2020).
[72] Whang A J W, Chen Y Y, Chang C M et al. Prediction technique of aberration coefficients of interference fringes and phase diagrams based on convolutional neural network[J]. Optics Express, 28, 37601-37611(2020).
[73] Liu X, Yang Z M, Dou J T et al. Fast demodulation of single-shot interferogram via convolutional neural network[J]. Optics Communications, 487, 126813(2021).
[74] Lin X, Rivenson Y, Yardimci N T et al. All-optical machine learning using diffractive deep neural networks[J]. Science, 361, 1004-1008(2018).
[75] Goi E, Schoenhardt S, Gu M. Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks[J]. Nature Communications, 13, 7531(2022).
[76] Pan X S, Zuo H, Bai H et al. Real-time wavefront correction using diffractive optical networks[J]. Optics Express, 31, 1067-1078(2023).
