Review of Optical Pre-Sensor Computing Technology and Its Satellite Remote Sensing Applications (Invited)

被引:1
|
作者
Li Tianyu [1 ]
Wang Guoqing [1 ]
Li Wei [2 ]
Chen Hongwei [3 ]
Liu Xun [2 ]
Wang Zhibin [1 ]
Liu Shaochong [1 ]
Cai Yanxin [2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China
[2] Beijing Inst Space Mech & Elect, Beijing 100094, Peoples R China
[3] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
关键词
optical pre-sensor computing; encoding and compression; all-optical intelligent inference; satellite remote sensing; VIDEO;
D O I
10.3788/LOP232509
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Optical pre- sensor computing is a technique involving information computation and processing in the optical domain at the front end of photoelectric sensors. This encompasses computation paradigms such as encoding compression and all- optical intelligent inference. It exhibits significant characteristics, such as computation during optical transmission and structure- function correlation, making it widely applicable in the field of satellite remote sensing. This paper introduces optical field modulation devices employed in pre-sensor computing, such as digital micromirror device ( DMD), liquid crystal spatial light modulator (LC-SLM), diffractive optical element (DOE), and metasurface. Subsequently, we systematically review the pertinent technological advancements in pre-sensor encoding compression and all-optical intelligent inference. Finally, the application pathways and future development trends of optical pre-sensor computing in the field of satellite remote sensing are discussed.
引用
收藏
页数:15
相关论文
共 110 条
  • [1] Single-shot optical neural network
    Bernstein, Liane
    Sludds, Alexander
    Panuski, Christopher
    Trajtenberg-Mills, Sivan
    Hamerly, Ryan
    Englund, Dirk
    [J]. SCIENCE ADVANCES, 2023, 9 (25)
  • [2] Boyd P S, 2010, Machine Learning, V3, P128
  • [3] Reinforcement learning in a large-scale photonic recurrent neural network
    Bueno, J.
    Maktoobi, S.
    Froehly, L.
    Fischer, I.
    Jacquot, M.
    Larger, L.
    Brunner, D.
    [J]. OPTICA, 2018, 5 (06): : 756 - 760
  • [4] Cai YH, 2023, Arxiv, DOI arXiv:2305.10299
  • [5] Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
    Cai, Yuanhao
    Lin, Jing
    Hu, Xiaowan
    Wang, Haoqian
    Yuan, Xin
    Zhang, Yulun
    Timofte, Radu
    Van Gool, Luc
    [J]. COMPUTER VISION - ECCV 2022, PT XVII, 2022, 13677 : 686 - 704
  • [6] Cai YH, 2022, Arxiv, DOI arXiv:2205.10102
  • [7] Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
    Cai, Yuanhao
    Lin, Jing
    Hu, Xiaowan
    Wang, Haoqian
    Yuan, Xin
    Zhang, Yulun
    Timofte, Radu
    Van Gool, Luc
    [J]. 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 17481 - 17490
  • [8] Photonic Neural Networks and Its Applications
    Chen Bei
    Zhang Zhaoyang
    Dai Tingge
    Yu Hui
    Wang Yuehai
    Yang Jianyi
    [J]. LASER & OPTOELECTRONICS PROGRESS, 2023, 60 (06)
  • [9] Chen H, 2022, Research on multimode fiber imaging based on phase modulation and deep learning
  • [10] Convexificators for nonconvex multiobjective optimization problems with uncertain data: robust optimality and duality
    Chen, J. W.
    Yang, R.
    Kobis, E.
    Ou, X.
    [J]. OPTIMIZATION, 2023,