High Power Laser and Particle Beams, Volume. 36, Issue 6, 069002(2024)

Deep learning phase inversion technique for single frame image based on Walsh function modulation

Qi Liu1...2, Yinglei Du1,*, Rujian Xiang1, Guohui Li1, Qiushi Zhang1, Zhenjiao Xiang1, Jing Wu1, Xian Yue1, Anchao Bao1, and Jiang You12 |Show fewer author(s)
Author Affiliations
  • 1Institute of Applied Electronics, CAEP, Mianyang 621900, China
  • 2Graduate School of China Academy of Engineering Physics, Beijing 100088, China
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    Figures & Tables(15)
    Two-dimensional polar representation of Walsh function
    Wavefront and far field intensity plots for positive and negative defocusing aberrations after modulation using partial Walsh functions
    Network Residual unit
    Experimental flowchart
    Predicted average time per frame
    Train loss curve
    PV、RMS、RMS ratio scatter plot
    Original wavefront restoration results
    Rotated 180° complex conjugate wavefront restoration results
    Example image of setting a masking template
    Random noise sample images
    • Table 1. Comparison of test results of different Walsh function modulated sample training networks

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      Table 1. Comparison of test results of different Walsh function modulated sample training networks

      Walsh functionResNet18 RMSE/μmResNet34 RMSE/μmResNet50 RMSE/μmResNet101 RMSE/μm
      W120.01150.01050.00800.0069
      W150.01110.00920.00620.0067
      W30.01030.00870.00550.0054
    • Table 2. Average RMS ratio with Zernike order wavefront

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      Table 2. Average RMS ratio with Zernike order wavefront

      Zernike levelsaverage RMS ratio/%
      Zernike 4~109.6
      Zernike 4~158.6
      Zernike 4~208.5
      Zernike 4~258.4
      Zernike 4~307.8
    • Table 3. Average RMS ratio of far-field intensity map with different pixels

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      Table 3. Average RMS ratio of far-field intensity map with different pixels

      intensity image pixelsaverage RMS ratio/%
      256×2567.8
      512×51210.2
      1024×102412.9
    • Table 4. Average RMS ratio of noise samples under different models

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      Table 4. Average RMS ratio of noise samples under different models

      test data setsamples for training purposesaverage RMS ratio of residual wavefront to original wavefront/%
      256×256 with random noise samples350 000 normal samplesinvalid
      256×256 with random noise samples350 000 normal samples+ 350 000 noise samples6.5
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    Qi Liu, Yinglei Du, Rujian Xiang, Guohui Li, Qiushi Zhang, Zhenjiao Xiang, Jing Wu, Xian Yue, Anchao Bao, Jiang You. Deep learning phase inversion technique for single frame image based on Walsh function modulation[J]. High Power Laser and Particle Beams, 2024, 36(6): 069002

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    Paper Information

    Category: Advanced Interdisciplinary Science

    Received: Feb. 1, 2024

    Accepted: Mar. 28, 2024

    Published Online: Jun. 3, 2024

    The Author Email: Du Yinglei (boyduyinglei@163.com)

    DOI:10.11884/HPLPB202436.240048

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