Advanced Imaging, Volume. 1, Issue 3, 031001(2024)

Multi-polarization fusion network for ghost imaging through dynamic scattering media Editors' Pick

Xin Lu1、†, Zhe Sun2、*, Yifan Chen2, Tong Tian3,4, Qinghua Huang2、*, and Xuelong Li2,5
Author Affiliations
  • 1School of Computer Science, Northwestern Polytechnical University, Xi’an, China
  • 2School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi’an, China
  • 3Institute of Optics and Quantum Electronics, Abbe Center of Photonics, Friedrich Schiller University, Jena, Germany
  • 4Helmholtz Institute Jena, Jena, Germany
  • 5Institute of Artificial Intelligence (TeleAI), China Telecom, Shanghai, China
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    Figures & Tables(10)
    Schematic diagram of the MPFNet.
    Schematic diagram of the MBF module.
    Schematic diagram of the MBSCA structure.
    Schematic diagram of the experimental setup. (a) The free-space ghost imaging experimental setup. The laser passes through a lens and shines on the DMD. The laser passes through the object and is received by the SPD. (b) The underwater ghost imaging experimental setup. The light source is modulated by a PBS and a QWP. The dashed box in the first part represents the acquisition of linear polarization signals, and the dashed box in the back represents the acquisition of circular polarization signals.
    Reconstruction results in free space, with the corresponding grayscale and ground truth of the object.
    Quantitative evaluations of the CNR, PSNR, and resolution of TGI, DGI, and the MPFNet. Object-DGI and Object-TGI represent the reconstruction results of DGI and TGI at different sampling rates. Object-Ours-s represents the single input and Object-Ours-m represents the dual-branch input.
    (a) Relationship between CNR and resolution for images reconstructed by the network using different speckle sizes. (b) Relationship between CNR and resolution for DGI reconstruction results of light fields modulated with various speckle sizes in underwater environments.
    Reconstructed image results using simulated data. Ours-s represents single-branch imaging results; Ours-m represents dual-branch imaging results including MBF and MBSCA structures.
    (a) Reconstructed underwater ghost images and corresponding CNR, PSNR, and resolution. ResUNet-LP-∥, ResUNet-LP-⊥, and ResUNet-CP represent the results of using ResUNet to image the two vertical components of linear polarization and circular polarization data, respectively. GIDC-LP-∥, GIDC-LP-⊥, and GIDC-CP represent the results of imaging the two perpendicular components of linear polarization and circular polarization data using GIDC, respectively. LP-∥ represents the result with horizontal linear polarization. LP-⊥ represents the result with vertical linear polarization. Multi-LP represents the fusion of perpendicular linear polarization components. CP represents circular polarization. LP + CP represents the result of the fusion of linear and circular polarizations. We highlight the imaging results of ResUNet and GIDC and the imaging results of our proposed method with the green solid box and the red solid box. (b) The fluctuations of 1500 data points collected for the letter "P" under two linear polarization directions.
    • Table 1. MPFNet Algorithm.

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      Table 1. MPFNet Algorithm.

      MPFNet Algorithm: The initial weight value σ is 0, σstep is 1/N, γ is 0.8, the learning rate is 0.05, and the momentum parameter β1 and epsilon parameter β2 in the batch normalization are set to 0.5 and 0.09, respectively. The Leaky ReLu activation function, with a leak parameter of 0.2, is used for each layer. The term LossTV represents the total variation regularization.
      Input:PN(x,y),IN1(real), and IN2(real)
      Output:img
      1: Initialize: randomly initialize the parameters θ of fMPFNet
      2: 1D signal1=IN1(real)
      3: 1D signal2=IN2(real)
      4: for step = 1,2,,Ndo
      5: img1=fMPFNB1[IN1(real)]
      6: img2=fMPFNB2[IN2(real)]
      7: Loss1=PN(x,y)·img1IN1(real)2+LossTV(img1)
      8: Loss2=PN(x,y)·img2IN2(real)2+LossTV(img2)
      9: LossMSE=Loss1Loss22/N
      10: LossMPFNet=σLossMSE+(1σ)(Loss1+Loss2)
      11: θ=Adam(θ,LossMPFNet,lγ,β1,β2)
      12: ifσγthen
      13: σ=σ+σstep
      14: end if
      15: img =(img1+img1)/2
      16: end for
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    Xin Lu, Zhe Sun, Yifan Chen, Tong Tian, Qinghua Huang, Xuelong Li, "Multi-polarization fusion network for ghost imaging through dynamic scattering media," Adv. Imaging 1, 031001 (2024)

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    Paper Information

    Category: Research Article

    Received: Aug. 30, 2024

    Accepted: Nov. 5, 2024

    Published Online: Dec. 10, 2024

    The Author Email: Sun Zhe (sunzhe@nwpu.edu.cn), Huang Qinghua (qhhuang@nwpu.edu.cn)

    DOI:10.3788/AI.2024.10014

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