基于深度学习技术从散斑场中识别
多涡旋结构的轨道角动量

doi:10.3969/j.issn.1007-5461.2022.06.009

量子电子学报 ›› 2022, Vol. 39 ›› Issue (6): 955-961.doi: 10.3969/j.issn.1007-5461.2022.06.009

• "光电探测与成像新技术及应用"专辑 • 上一篇下一篇

基于深度学习技术从散斑场中识别多涡旋结构的轨道角动量

王孝艳, 王志远, 陈子阳, 蒲继雄∗

华侨大学信息科学与工程学院, 福建省光传输与变换重点实验室, 福建厦门 361021

收稿日期:2022-06-23 修回日期:2022-07-29 出版日期:2022-11-28 发布日期:2022-12-14
通讯作者: E-mail: jixiong@hqu.edu.cn E-mail:E-mail: jixiong@hqu.edu.cn
作者简介:王孝艳 ( 1986 - ), 硕士, 实验师, 主要从事光电成像技术及智能算法方面的研究。 E-mail: xiaoyan−wang−3@foxmail.com
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 62005086)

Detection of orbital angular momentum of multiple vortices from speckle via deep learning

WANG Xiaoyan, WANG Zhiyuan, CHEN Ziyang, PU Jixioing ∗

( Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science & Engineering, Huaqiao University, Xiamen 361021, China )

Received:2022-06-23 Revised:2022-07-29 Published:2022-11-28 Online:2022-12-14

摘要/Abstract

摘要： 涡旋光束的轨道角动量 (OAM) 可用于信息的编码, 因此在自由空间光通讯等领域具有重要的应用价值。然而, 实际的传输空间通常存在着各种随机介质, 会造成传输涡旋光束的波面畸变, 导致传统的方法无法准确测量涡旋光束的轨道角动量。针对此问题, 以毛玻璃作为随机介质, 基于深度学习技术, 从涡旋光束经过毛玻璃所产生的散斑场中准确识别出了涡旋光束的轨道角动量。进一步, 为提升光信息的编码与传输能力, 还测试了多涡旋结构光束的轨道角动量识别。测试结果表明, 对于五个涡旋结构的光束, 所设计的网络也能从单帧散斑图中准确识别其轨道角动量。

关键词: 量子光学, 轨道角动量, 深度学习, 涡旋光束, 散斑

Abstract: The orbital angular momentum (OAM) of vortex beams can be used for information coding, which has important applications in free space optical communications. Nevertheless, the propagation of beams may encounter various random media in actual transmission space, which will distort the wavefront of vortex beam, rendering the ineffectiveness of traditional methods in retrieving the OAM. To tackle this issue, by taking ground glass as a random medium, recognition of OAM of vortex beam from speckle pattern is realized based on deep learning. Moreover, to further upgrade the information coding and transmission capability, recognition of multiple OAMs is examined as well. The results show that the designed network is capable of simultaneously recognizing five OAMs from a single-shot speckle pattern generated by vortex beams through ground glass.

Key words: quantum optics, orbital angular momentum, deep learning, vortex beam, speckle

中图分类号:

O431.2

王孝艳, 王志远, 陈子阳, 蒲继雄∗. 基于深度学习技术从散斑场中识别多涡旋结构的轨道角动量[J]. 量子电子学报, 2022, 39(6): 955-961.

WANG Xiaoyan, WANG Zhiyuan, CHEN Ziyang, PU Jixioing ∗. Detection of orbital angular momentum of multiple vortices from speckle via deep learning[J]. Chinese Journal of Quantum Electronics, 2022, 39(6): 955-961.

参考文献

[1] L. Allen, M. W. Beijersbergen, R. J. C. Spreeuw, and J. P. Woerdma, Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes, Phys. Rev. A 45, 8185 (1992).
[2] M. Padgett, and R. Bowman, Tweezers with a twist, Nat. Photonics 5, 343–348 (2011) D. G. Grier, A revolution in optical manipulation, Nature 424, 810–816 (2003).
[3] G. Gibson, J. Courtial, M.J. Padgett. Free-space information transfer using light beams carrying orbital angular momentum [J] Opt. Express 2004, 22: 5448-5456.
[4] C. Paterson, Atmospheric turbulence and orbital angular momentum of single photons for optical communication, Phys. Rev. Lett. 94, 153901 (2005).
[5] A. M. Yao, and M. J. Padgett, Orbital angular momentum: origins, behavior and applications, Advances in optics and photonics 3, 161-204 (2011).
[6] J. Wang, et al., Terabit free-space data transmission employing orbital angular momentum multiplexing, Nat, Photonics 6, 488–496 (2012).
[7] X. Qiu, F. Li, W. Zhang, Z, Zhu, and L. Chen, Spiral phase contrast imaging in nonlinear optics: seeing phase objects using invisible illumination, Optica 5, 208-212 (2018).
[8] 胡海峰,詹其文.基于光子轨道角动量的手性测量方法[J].量子电子学报,2022,v.39; No.205(02):272-285.
[9] Z. Liu, S. Yan, H. Liu, and X. Chen Superhigh-resolution recognition of optical vortex modes assisted by a deep-learning method, Phys. Rev. Lett. 123, 183902 (2019).
[10] M. Padgett, J. Courtial, and L. Allen, Light's orbital angular momentum, Phys. Today, 57, 35-40 (2004).
[11] J. Leach, M. J. Padgett, S. M. Barnett, S. Franke-Arnold, J. Courtial, Measuring the Orbital Angular Momentum of a Single Photon, Phys. Rev. Lett. 88, 257901 (2002).
[12] H. I. Sztul, and R. R. Alfano, Double-slit interference with Laguerre Gaussian beams, Opt. Lett. 31, 999 (2006).
[13] Q. Zhao, M. Dong, Y. Bai, and Y. Yang, Measuring high orbital angular momentum of vortex beams with improved multipoint interferometer, Photon. Res. 8, 745 (2020) .
[14] J. M. Hickmann, E. J. S. Fonseca, W. C. Soares, and S. Chavez-Cerda, Unveiling a truncated optical lattice associated with a triangular aperture using light's orbital angular momentum, Phys. Rev. Lett. 105, 053904 (2010).
[15] R. Sahu, S. Chaudhary, K. Khare, M. Bhattacharya, H. Wanare, and A. K. Jha, Angular lens, Opt. Express 26, 8709-8718 (2018).
[16] Fan W, Chen Z, Yakovlev V V, et al. High‐Fidelity Image Reconstruction through Multimode Fiber via Polarization‐Enhanced Parametric Speckle Imaging[J]. Laser & Photonics Reviews, 2021, 15(5): 2000376.
[17] 高敬敬,刘红林,王歆,韩申生.毛玻璃和体散射介质的散射等效性对比研究[J].光学学报,2021,41(17):181-188.
[18] 王玉双,陈子阳,刘永欣,蒲继雄.激光经过湍流大气以及散射介质的传输[J].量子电子学报,2020,v.37; No.196(05):534-546.
[19] S. M. Popoff, G. Lerosey, M. Fink, A. C. Boccara, S. Gigan, New J.Phys. 2011, 13, 123021. S.Popoff,G.Lerosey,M.Fink,A.C.Boccara,S.Gigan,Nat.Commun.2010, 1, 81.
[20] Chen L, Singh R K, Dogariu A, et al. Estimating topological charge of propagating vortex from single-shot non-imaged speckle[J]. Chinese Optics Letters, 2021, 19(2): 022603.
[21] Raskatla V, Singh B P, Patil S, et al. Speckle-based deep learning approach for classification of orbital angular momentum modes[J]. JOSA A, 2022, 39(4): 759-765.
[22] Raskatla V, Badavath P S, Kumar V. Convolutional networks for speckle-based orbital angular momentum modes classification[J]. Optical Engineering, 2022, 61(3): 036114.

基于深度学习技术从散斑场中识别多涡旋结构的轨道角动量

Detection of orbital angular momentum of multiple vortices from speckle via deep learning

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈骊颖, 黄堃, 王琪, 闫仕农 . 基于金刚石NV色心集成振动检测模块的设计[J]. 量子电子学报, 2023, 40(4): 500-509.
[2]	摆海龙, 白金海, 胡栋, 王宇. 用于原子干涉重力仪的小型频率合成器设计与实现[J]. 量子电子学报, 2023, 40(4): 510-518.
[3]	李嵩松. 玻色-爱因斯坦凝聚体中三体和四体相互作用对自旋压缩和量子纠缠的影响研究[J]. 量子电子学报, 2023, 40(4): 519-527.
[4]	李艳, . 一维强相互作用的玻色-费米混合气体关联特性研究[J]. 量子电子学报, 2023, 40(4): 528-540.
[5]	王晟, 方晓明, 林昱, 张天兵, 冯宝, 余杨, 王乐 . 四强度诱骗态相位匹配量子密钥分发协议[J]. 量子电子学报, 2023, 40(4): 541-545.
[6]	孙一石, 孙弋 . 基于BP 神经网络的经典-量子信号共纤同传系统参数预测[J]. 量子电子学报, 2023, 40(4): 546-559.
[7]	戚志明, 梁文耀 . 光束偏振对全息干涉制作复式光子晶体的影响研究[J]. 量子电子学报, 2023, 40(4): 447-457.
[8]	何业锋, 李丽娜∗, 白倩, 陈思昊, 强雨薇 . 标记单光子源下探测器死时间的量子密钥分配[J]. 量子电子学报, 2023, 40(1): 112-119.
[9]	郭辉∗ , 叶知秋. 随机散斑图正交优化计算鬼成像[J]. 量子电子学报, 2023, 40(1): 48-55.
[10]	谈志杰杨海瑞喻虹韩申生. X光强度关联衍射成像技术研究进展[J]. 量子电子学报, 2022, 39(6): 851-862.
[11]	林惠祖刘伟涛孙帅杜隆坤常宸李月刚. 关联成像算法研究进展[J]. 量子电子学报, 2022, 39(6): 863-879.
[12]	林冰 , 樊学强 , 李德奎 , 彭志勇 , 郭忠义∗. 基于深度学习的散射光场成像研究进展[J]. 量子电子学报, 2022, 39(6): 880-898.
[13]	陈建明, , 曾祥津, , 钟丽云, , 邸江磊, ∗ , 秦玉文, ∗. 基于深度学习的图像配准方法研究进展综述[J]. 量子电子学报, 2022, 39(6): 899-926.
[14]	马慧敏 ∗ , 檀磊 , 张京会 , 张鹏飞 , 宁孝梅 , 刘海秋 , 高彦伟 . 基于深度学习的合成孔径成像系统共相误差检测研究综述[J]. 量子电子学报, 2022, 39(6): 927-941.
[15]	李能菲孙宇松黄见. 余弦编码复用高空间分辨率关联成像研究[J]. 量子电子学报, 2022, 39(6): 973-982.

基于深度学习技术从散斑场中识别 多涡旋结构的轨道角动量

Detection of orbital angular momentum of multiple vortices from speckle via deep learning

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

基于深度学习技术从散斑场中识别多涡旋结构的轨道角动量