Laser & Optoelectronics Progress, Volume. 60, Issue 10, 1030002(2023)

Filtering Hyperspectral Imaging Technology Based on Deep Learning

Xueli Lin1,2, Zilin Wang1,2, Yanxia Zou1,2, Hao Liu1,2, Ran Hao1,2, and Shangzhong Jin1,2、*
Author Affiliations
  • 1College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, Zhejiang, China
  • 2Key Laboratory of Zhejiang Province on Modern Measurement Technology and Instruments, Hangzhou 310018, Zhejiang, China
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    Figures & Tables(7)
    Spectral imaging model. (a) Schematic of principle; (b) schematic of network
    Simulation of light modulation by filter and camera based on LN layer
    Imaging principle of PS Kappa DX4 hyperspectral camera
    Comparison of the spectral reconstruction performance of the optimized model with two structural filter schemes. (a) RGB image, tf MRAE and nl MRAE images, tf MRAE hist and nl MRAE hist images; (b) reconstructed irradiance of some pixels in sample No. 3
    Comparison of the optimized filter's spectral response function. (a) Thin film interference filter scheme; (b) no-limited structure filter scheme
    Impact of various array sizes on the spectral imaging performance of no-limited structure filters. (a) RGB image, tf MRAE and nl MRAE images, tf MRAE hist and nl MRAE hist images; (b) reconstructed irradiance of some pixels in sample No. 3
    • Table 1. Structure of NET2

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      Table 1. Structure of NET2

      LayerInputOuput
      BNn2n2
      PReLUn2n2
      LNn2600
      BN600600
      PReLU600600
      LN600300
      BN300300
      PReLU300300
      LN300100
      BN100100
      PReLU100100
      LN10030
      BN3030
      PReLU3030
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    Xueli Lin, Zilin Wang, Yanxia Zou, Hao Liu, Ran Hao, Shangzhong Jin. Filtering Hyperspectral Imaging Technology Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1030002

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    Paper Information

    Category: Spectroscopy

    Received: Mar. 14, 2022

    Accepted: Apr. 19, 2022

    Published Online: May. 17, 2023

    The Author Email: Shangzhong Jin (jinsz@cjlu.edu.cn)

    DOI:10.3788/LOP220984

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