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深度学习算法及其在光学的应用

周宏强 黄玲玲 王涌天

周宏强, 黄玲玲, 王涌天. 深度学习算法及其在光学的应用[J]. 红外与激光工程, 2019, 48(12): 1226004-1226004(20). doi: 10.3788/IRLA201948.1226004
引用本文: 周宏强, 黄玲玲, 王涌天. 深度学习算法及其在光学的应用[J]. 红外与激光工程, 2019, 48(12): 1226004-1226004(20). doi: 10.3788/IRLA201948.1226004
Zhou Hongqiang, Huang Lingling, Wang Yongtian. Deep learning algorithm and its application in optics[J]. Infrared and Laser Engineering, 2019, 48(12): 1226004-1226004(20). doi: 10.3788/IRLA201948.1226004
Citation: Zhou Hongqiang, Huang Lingling, Wang Yongtian. Deep learning algorithm and its application in optics[J]. Infrared and Laser Engineering, 2019, 48(12): 1226004-1226004(20). doi: 10.3788/IRLA201948.1226004

深度学习算法及其在光学的应用

doi: 10.3788/IRLA201948.1226004
基金项目: 

北京市卓越青年科学家项目(BJJWZYJH01201910007022);国家自然科学基金面上项目(61775019);北京市科技新星(Z171100001117047);北京市面上项目(4172057);教育部霍英东高校教师基金(161009)

详细信息
    作者简介:

    周宏强(1992-),男,博士生,主要从事深度学习及微纳光学方面的研究。Email:hzhou@bit.edu.cn

    通讯作者: 黄玲玲(1986-),女,教授,博士生导师,博士,主要从事微纳光学、计算全息、表面等离激元及深度学习等方面的研究。Email:huanglingling@bit.edu.cn
  • 中图分类号: TN202

Deep learning algorithm and its application in optics

  • 摘要: 深度学习作为机器学习的重要分支,自出现之初就掀起了机器学习的又一次高潮。深度学习在诸如图像识别与分类、语义分割、智能驾驶等多个领域有着优异的表现。同时,深度学习算法以其抽象特征识别和提取特性,极强的模型构建和泛化推广能力,被广泛应用于光学领域,如计算全息图产生与成像、数字全息的无参数重建和光谱共振曲线预测等方面。详细介绍了深度学习的基本原理及在图像分类、超分辨成像、计算全息和数字全息、表面等离激元共振曲线预测、超表面的结构设计等方面的典型应用研究,并探讨了深度学习在物理光学领域未来值得研究的方向。
  • [1] Simon H. Machines, Neural Network and Learning[M]. 3rd ed. New Jersey:Pearson Education, Inc, 2009.
    [2] Simon H. A Comprehensive Foundation[M]. 2nd ed. New Jersey:Prentice Hall International, Inc, 1999.
    [3] Gardner M W, Dorling S R. Artificial neural networks(the multilayer perceptron)-a review of application in the atmospheric sciences[J]. Atmospheric Environment. 1998, 32(14):2627-2636.
    [4] Hornic K. Multilayer feedforward networks are universal approximators[J]. Neural Network, 1989, 2(5):359-366.
    [5] Arel I, Rose D C, Karnowski T P. Deep machine learning-a new frontier in artificial intelligence research[J]. IEEE Computational Intelligence Magazine, 2010, 5(4):13-18.
    [6] Qiao Fengjuan, Guo Hongli, Li Wei, et al. Research on deep learning classification based on SVM:a review[J]. Journal of Qilu University of Technology, 2018, 32(5):39-44. (in Chinese)
    [7] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3):273-297.
    [8] Lecun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
    [9] Lecun Y, Boser B E, Denker J S, et al. Handwritten digit recognition with a back-propagation network[C]//Proc Advances in Neural Information Processing Systems, 1990:396-404.
    [10] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[C]//Proceedings of IEEE. 1998, 86(11):2278-2324.
    [11] Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
    [12] Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv Preprint, 2014, arXiv:1406.1078.
    [13] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//Advances in Neural Information Processing Systems, 2014:2672-2680.
    [14] Yoav F, David H. Unsupervised learning of distributions of binary vectors using 2 layer networks[C]//Advances in Neural Information Processing Systems, 1991, 4:912-918.
    [15] Hinton G E, Osindero S, Teh Y. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.
    [16] Wang Bingrui, Lan Huiying, Chen Yunji. Programming frameworks for deep learning algorithms[J]. Big Data Research, 2018, 4(4):56-63. (in Chinese)
    [17] Jake B. Notes on convolutional neural networks[D]. Cambridge:MIT Press, 2006.
    [18] Lecun Y, Bengio Y. Convolutional Networks for Images, Speech, and Time Series[M]. Cambridge:MIT Press, 1995.
    [19] Waibel A, Hanazawa T, Hinton G, et al. Phoneme recognition using time-delay neural networks[J]. IEEE Transactions on Acoustics Speech and Signal Processing, 1989, 37(3):328-339.
    [20] Bottou L, Fogelman-Soubi F, Blanchet P, et al. Experiments with time delay networks and dynamic time warping for speaker independent isolated digit recognition[C]//Euro Speech:1989, 89:537-540.
    [21] Simard P Y, Steinkraus D, Platt J C. Best practices for convolutional neural networks applied to visual document analysis[C]//Seventh International Conference on Document Analysis and Recognition, 2003:958-962.
    [22] Vaillant R, Monrocq C, Lecun Y. Original approach for the localization of objects in images[J]. IEEE Proceedings-Vision Image and Signal Proceeding, 1994, 141(4):245-250.
    [23] Nowlan S, Platt J. A convolutional neural network hand tracker[C]//Advances Neural Information Processing Systems, 1995:901-908.
    [24] Lawrence S, Giles C L, Tsoi A C, et al. Face recognition:A convolutional neural-network approach[J]. IEEE Transactions on Neural Networks, 1997, 8(1):98-113.
    [25] Goodfellow Ian, Bengio Yoshua, Courville Aaron. Deep Learing[M]. Translated by Zhao S, Li Y, Fu T, et al. Beijing:Post and Telecom Press, 2017:274-298. (in Chinese)
    [26] Shin H, Roth H R, Gao M, et al. Deep convolutional neural networks for computer-aided detection:CNN architectures, dataset characteristics and transfer learning[J]. IEEE Transactions on Medical Imaging, 2016, 35(5SI):1285-1298.
    [27] He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9):1904-1916.
    [28] Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis[J]. Medical Image Analysis, 2017, 42:60-88.
    [29] Esteva A, Kuprel B, Novoa R A, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639):115.
    [30] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651.
    [31] Murtagh P, Tsoi A C. Implementation issues of sigmoid function and its derivative for VLSI digital neural networks[J]. IEEE Proceedings-E Computers and Digital Techniques, 1992, 139(3):207-214.
    [32] Xu G, Wu H, Shi Y. Structural design of convolutional neural networks for steganalysis[J]. IEEE Signal Processing Letters, 2016, 23(5):708-712.
    [33] Han J, Cho G, Kwak K. A design of convolutional neural network using ReLU-based ELM classifier and its application[C]//Proceedings of the 9th International Conference on Machine Learning and Computing, 2017.
    [34] Mirza M O S. Conditional generative adversarial nets[J]. arXiv preprint, 2014, arXiv:1411.1784.
    [35] Radford A M L C S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. arXiv preprint, 2015, arXiv:1511. 06434.
    [36] Reed S, Akata Z, Yan X, et al. Generative adversarial text to image synthesis[J]. arXiv preprint, 2016, arXiv:1605.05396.
    [37] Wu J, Zhang C, Xue T, et al. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modelin[J]. arXiv preprint, 2016, arXiv:1610.07584.
    [38] Smith E, Meger D. Improved adversarial systems for 3D object generation and reconstruction[J]. arXiv preprint. 2017, arXiv:1707.09557.
    [39] Denton E, Gross S, Fergus R. Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks[J]. arXiv preprint, 2016, arXiv:1611.06430.
    [40] Yi Z, Zhang H, Tan P, et al. DualGAN:Unsupervised dual learning for image-to-image translation[J]. arXiv preprint, 2017, arXiv:1704. 02510.
    [41] Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network[J]. arXiv preprint, 2017, arXiv:1609. 04802v5.
    [42] Vondrick C, Pirsiavash H, Torralba A. Generating videos with scene dynamics[J]. arXiv preprint, 2016, arXiv:1609.02612v3.
    [43] Perarnau G, van de Weijer J, Raducanu B, et al. Invertible conditional GANs for image editing[J]. arXiv preprint, 2016, arXiv:1611.06355.
    [44] Antonia Creswell A A B. Inverting the generator of a generative adversarial network[J]. arXiv preprint, 2016, arXiv:1611.05644.
    [45] Zhou S, Xiao T, Yang Y, et al. GeneGAN:learning object transfiguration and attribute subspace from unpaired data[J]. arXiv preprint, arXiv:1705.04932.
    [46] Kim T, Cha M, Kim H, et al. Learning to discover cross-domain relations with generative adversarial networks[J]. arXiv preprint, 2017, arXiv:1703.05192.
    [47] Wang C, Wang C, Xu C, et al. Tag disentangled generative adversarial network for object image re-rendering[C]//The Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017:2901-2907.
    [48] Antipov G, Dugelay M B J. Face aging with conditional generative adversarial networks[J]. arXiv preprint, 2017, arXiv:1702.01983.
    [49] Arjovsky M, Chintala S, Bottou L. Wasserstein GAN[J]. arXiv preprint, 2017, arXiv:1701.07875.
    [50] Ando T, Horisaki R, Tanida J. Speckle-learning-based object recognition through scattering media[J]. Optics Express, 2015, 23(26):33902-33910.
    [51] Horisaki R, Takagi R, Tanida J. Learning based imaging through scattering media[J]. Optics Express, 2016, 24(13):13738-13743.
    [52] Takagi R, Horisaki R, Tanida J. Object recognition through a multi-mode fiber[J]. Optical Review, 2017, 24(2):117-120.
    [53] Horisaki R, Takagi R, Tanida J. Learning based focusing through scattering media[J]. Applied Optics, 2017, 56(15):4358-4362.
    [54] Horisaki R, Takagi R, Tanida J. Learning based single-shot superresolution in diffractive imaging[J]. Applied Optics, 2017, 56(32):8896-8901.
    [55] Sinha A, Lee J, Li S, et al. Lensless computational imaging through deep learning[J]. Optica, 2017, 4(9):1117-1125.
    [56] Li R, Zeng T, Peng H, et al. Deep learning segmentation of optical microscopy images improves 3-D neuron reconstruction[J]. IEEE Transactions on Medical Imaging, 2017, 36(7):1533-1541.
    [57] Lyu M, Wang W, Wang H, et al. Deep-learning-based ghost imaging[J]. Scientific Reports, 2017, 7:17865.
    [58] Jo Y, Park S, Jung J, et al. Holographic deep learning for rapid optical screening of anthrax spores[J]. Science Advances, 2017, 3(8):e1700606.
    [59] Lin X, Rivenson Y, Yardimei N T, et al. All-optical machine learning using diffractive deep neural networks[J]. Science, 2018, 361(6406):1004.
    [60] Xiao H, Rasul K, Vollgraf R, et al. fashion-minist[EB/OL]. San Francisco:GitHub database site, 2017[2019-12-17]. https://github.com/zalandoresearch/fashion-mnist#fashion-mnist.htm.
    [61] Li Jingxi, Mengu Deniz, Luo Yi, et al. Class-specific differential detection in diffractive optical neural networks improves inference accuracy[J]. Advanced Photonics, 2019, 1(4):046001.
    [62] Huang B, Bates M, Zhuang X. Super resolution fluorescence microscopy[J]. Annual Review of Biochemistry, 2009, 78:993-1016.
    [63] Willig K I, Rizzoli S O, Westphal V, et al. STED microscopy reveals that synaptotagmin remains clustered after synaptic vesicle exocytosis[J]. Nature, 2006, 440(7086):935-939.
    [64] Rittweger E, Han K Y, Irvine S E, et al. STED microscopy reveals crystal colour centres with nanometric resolution[J]. Nature Photonics, 2009, 3(3):144-147.
    [65] Rust M J, Bates M, Zhuang X. Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)[J]. Nature Methods, 2006, 3(10):793-795.
    [66] Dong C, Loy C C, He K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2):295-307.
    [67] Rivenson Y, Gorocs Z, Gunaydin H, et al. Deep learning microscopy[J]. Optica, 2017, 4(11):1437-1443.
    [68] Wu Y, Rivenson Y, Zhang Y, et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery[J]. Optica, 2018, 5(6):704-710.
    [69] Rivenson Y, Zhang Y, Gnaydin H, et al. Phase recovery and holographic image reconstruction using deep learning in neural networks[J]. Light-Science Applications, 2018, 7:e17141.
    [70] Huang L, Muhlenbernd H, Li X, et al. Broadband hybrid holographic multiplexing with geometric metasurfaces[J]. Advanced Materials, 2015, 27(41):6444.
    [71] Huang L, Chen X, Muehlenbernd H, et al. Three-dimensional optical holography using a plasmonic metasurface[J]. Nature Communications, 2013, 4:2808.
    [72] Zijlstra P, Chon J W M, Gu M. Five dimensional optical recording mediated by surface plasmons in gold nanorods[J]. Nature, 2009, 459(7245):410-413.
    [73] Zhao R, Sain B, Wei Q, et al. Multichannel vectorial holographic display and encryption[J]. Light-Science Applications, 2018, 7:95.
    [74] Jiang Q, Jin G, Cao L. When metasurface meets hologram:principle and advances[J]. Advanced Optics and Photonics, 2019, 11(3):000518.
    [75] Goodman J W. Introduction to Fourier Optics[M]. 2nd ed. Singapore:McGraw-Hill, 1996:66-67.
    [76] Schnars U, Falldorf C, Watson J, et al. Digital Holography and Wavefront Sensing:Principles, Techniques and Applications[M]. Berlin:Springer, 2015.
    [77] Ren Z, Xu Z, Lam E Y. Learning-based nonparametric autofocusing or digital holography[J]. Optica, 2018, 5(4):337-344.
    [78] Slinger C, Cameron C, Stanley M. Computer-generated holography as a generic display technology[J]. Computer, 2005, 38(8):46-53.
    [79] Horisaki R, Takagi R, Tanida J. Deep-learning-generated holography[J]. Applied Optics, 2018, 57(14):3859-3863.
    [80] Pfeiffer C, Grbic A. Metamaterial huygens' surfaces:tailoring wave fronts with reflectionless sheets[J]. Physical Review Letters, 2013, 110(19):197401.
    [81] Arbabi A, Horie Y, Bagheri M, et al. Dielectric metasurfaces for complete control of phase and polarization with subwavelength spatial resolution and high transmission[J]. Nature Nanotechnology, 2015, 10(11):190-937.
    [82] Butet J, Brevet P, Martin O J F. Optical second harmonic generation in plasmonic nanostructures:from fundamental principles to advanced applications[J]. ACS Nano, 2015, 9(11):10545-10562.
    [83] Li G, Chen S, Pholchai N, et al. Continuous control of the nonlinearity phase for harmonic generations[J]. Nature Materials, 2015, 14(6):607-612.
    [84] Malkiel I, Mrejen M, Nagler A, et al. Plasmonic nanostructure design and characterization via deep learning[J]. Light-Science Applications, 2018, 7:60.
    [85] Liu D, Tan Y, Khoram E, et al. Training deep neural networks for the inverse design of nanophotonic structures[J]. ACS Photonics, 2018, 5(4):1365-1369.
    [86] Ma W, Cheng F, Liu Y. Deep learning enabled on-demand design of chiral metamaterials[J]. ACS Nano, 2018, 12(6):6326-6334.
    [87] Peurifoy J, Shen Y, Jing L, et al. Nanophotonic particle simulation and inverse design using artificial neural networks[J]. Science Advances, 2018, 4(6):aar4206.
    [88] Liu Z, Zhu D, Rodrigues S P, et al. Generative model for the inverse design of metasurfaces[J]. Nano Letters, 2018, 18(10):6570-6576.
    [89] Tahersima M H, Kojima K, Koike-Akino T, et al. Deep neural network inverse design of integrated photonic power splitters[J]. Scientific Reports, 2019, 9:1368.
    [90] Yao K, Unni R, Zheng Y. Intelligent nanophotonics:merging photonics and artificial intelligence at the nanoscale[J]. Nanophotonics, 2019, 8(3):339-366.
    [91] Su L, Piggott A Y, Sapra N V, et al. Inverse design and demonstration of a compact on-chip narrowband three-channel wavelength demultiplexer[J]. ACS Photonics, 2018, 5(2):301-305.
    [92] Piggott A Y, Lu J, Babinec T M, et al. Inverse design and implementation of a wavelength demultiplexing grating coupler[J]. Scientific Reports, 2014, 4:7210.
    [93] Molesky S, Lin Z, Piggott A Y, et al. Inverse design in nanophotonics[J]. Nature Photonics, 2018, 12(11):659-670.
    [94] Lan L, Sun F, Liu Y, et al. Experimentally demonstrated a unidirectional electromagnetic cloak designed by topology optimization[J]. Applied Physics Letters, 2013, 103(12):121113.
    [95] Fujii G, Watanabe H, Yamada T, et al. Level set based topology optimization for optical cloaks[J]. Applied Physics Letters, 2013, 102(25):251106.
    [96] Sell D, Yang J, Doshay S, et al. Periodic dielectric metasurfaces with high-efficiency, multiwavelength functionalities[J]. Advanced Optical Materials, 2017, 5(23):00645.
    [97] Deng Y, Korvink J G. Topology optimization for three-dimensional electromagnetic waves using an edge element-based finite-element method[C]//Proceedings of the Royal Society A-mathematical physical and engineering sciences, 2016, 472(2189):20150835.
    [98] Andkjaer J, Sigmund O. Topology optimized low-contrast all-dielectric optical cloak[J]. Applied Physics Letters, 2011, 98(2):021112.
    [99] Lin Z, Groever B, Capasso F, et al. Topology-optimized multilayered metaoptics[J]. Physical Review Applied, 2018, 9(4):044030.
    [100] Yin Baocai, Wang Wentong, Wang Litong. Review of deep learning[J]. Journal of Beijing University of Technology, 2015, 41(1):48-59. (in Chinese)
    [101] Barbastathis George, Ozcan Aydogan, Situ Guohai, On the use of deep learning for computational imaging[J]. Optica, 2019, 6(8):921-943.
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出版历程
  • 收稿日期:  2019-10-11
  • 修回日期:  2019-11-21
  • 刊出日期:  2019-12-25

深度学习算法及其在光学的应用

doi: 10.3788/IRLA201948.1226004
    作者简介:

    周宏强(1992-),男,博士生,主要从事深度学习及微纳光学方面的研究。Email:hzhou@bit.edu.cn

    通讯作者: 黄玲玲(1986-),女,教授,博士生导师,博士,主要从事微纳光学、计算全息、表面等离激元及深度学习等方面的研究。Email:huanglingling@bit.edu.cn
基金项目:

北京市卓越青年科学家项目(BJJWZYJH01201910007022);国家自然科学基金面上项目(61775019);北京市科技新星(Z171100001117047);北京市面上项目(4172057);教育部霍英东高校教师基金(161009)

  • 中图分类号: TN202

摘要: 深度学习作为机器学习的重要分支,自出现之初就掀起了机器学习的又一次高潮。深度学习在诸如图像识别与分类、语义分割、智能驾驶等多个领域有着优异的表现。同时,深度学习算法以其抽象特征识别和提取特性,极强的模型构建和泛化推广能力,被广泛应用于光学领域,如计算全息图产生与成像、数字全息的无参数重建和光谱共振曲线预测等方面。详细介绍了深度学习的基本原理及在图像分类、超分辨成像、计算全息和数字全息、表面等离激元共振曲线预测、超表面的结构设计等方面的典型应用研究,并探讨了深度学习在物理光学领域未来值得研究的方向。

English Abstract

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