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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

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文章导航 > 红外与激光工程  > 2019 >  48(12): 1226004-1226004(20)
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
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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
shu

深度学习算法及其在光学的应用

doi: 10.3788/IRLA201948.1226004
  • 1.

    北京理工大学 光电学院 光电成像技术与系统教育部重点实验室,北京 100081

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

  • 1.

    Key Laboratory of Photoelectronic Imaging Technology and System,Ministry of Education,School of Optics and Photonics,Beijing Institute of Technology,Beijing 100081,China

  • 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。
    Abstract: As an important branch of machine learning, deep learning has reached another climax of machine learning since its inception. Deep learning has excellent performance in many fields such as image recognition and classification, semantic segmentation, and intelligent driving and so on. At the same time, deep learning algorithms are widely used in the field of optics such as computational hologram generation and imaging, non-parameter reconstruction of digital holography, and spectral resonance curves prediction due to their abstract feature recognition and extraction characteristics, strong model building and generalization capabilities. This article detailed the basic principles of deep learning and its typical application research in image classification, super-resolution imaging, computer generated hologram and digital holography, prediction of surface plasmonics resonance curves, and structural design of metasurfaces. And future development of deep learning in the physical optical field was worth exploring.
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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
  • 1. 北京理工大学 光电学院 光电成像技术与系统教育部重点实验室,北京 100081
    • 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
  • 收稿日期: 2019-10-11
  • 修回日期: 2019-11-21
  • 刊出日期: 2019-12-25
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。

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