Acta Optica Sinica, Volume. 45, Issue 1, 0123002(2025)

Deep Learning Optimized Liquid Crystal Microlens Array Design for Hyperspectral Reconstruction Systems

Shiqi Li1,2, Hui Li1,2、*, Chuan Qiao1,2, Ting Zhu1,2, and Yuntao Wu1,2
Author Affiliations
  • 1School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430205, Hubei , China
  • 2Hubei Key Laboratory of Intelligent Robot, Wuhan 430205, Hubei , China
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    Figures & Tables(6)
    Schematic diagram of LCDE-IDN structure
    Flow chart of LCDE-IDN training and optimization
    Light field imaging device
    Results of LCDE-IDN. (a) Comparison of reconstructed spectral curves; (b) loss function change curves
    Schematic diagram of structure of LC-MLA and related experimental results. (a) Structural diagram of LC-MLA and actual sample diagram; (b) comparison of transmittance of empirical LC-MLA designed based on empirical method and optimized LC-MLA designed based on LCDE-IDN method at 3.0 V and 8.0 V, with wavelength range of 400 nm to 900 nm; (c) PSF comparison results of empirical LC-MLA and optimized LC-MLA at voltage of 4.8 V; (d)(e) light field imaging results based on empirical LC-MLA and optimized LC-MLA, respectively
    Comparison of spectral reconstruction results of optimized LC-MLA designed by LCDE-IDN method and empirical LC-MLA designed by empirical method (insets Ⅰ‒Ⅲ are comparison of true values and spectral reconstruction curves extracted from red, orange, and blue boxes on scene, respectively)
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    Shiqi Li, Hui Li, Chuan Qiao, Ting Zhu, Yuntao Wu. Deep Learning Optimized Liquid Crystal Microlens Array Design for Hyperspectral Reconstruction Systems[J]. Acta Optica Sinica, 2025, 45(1): 0123002

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    Paper Information

    Category: Optical Devices

    Received: Aug. 29, 2024

    Accepted: Oct. 14, 2024

    Published Online: Jan. 22, 2025

    The Author Email: Li Hui (lihui00317@163.com)

    DOI:10.3788/AOS241493

    CSTR:32393.14.AOS241493

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