Infrared and Laser Engineering, Volume. 54, Issue 5, 20240569(2025)

Laser interference image inpainting with global semantic perception and texture frequency domain constraints

Peiyao ZHAO1, Bin FENG1, Xinpeng YANG1, Xikui MIAO2, Yunlong WU3, and Qing YE3
Author Affiliations
  • 1School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
  • 263891 Unit of the Chinese People's Liberation Army, Luoyang 471300, China
  • 3College of Electronic Warfare, National University of Defense Technology, Hefei 230037, China
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    Figures & Tables(22)
    The network architecture in this article
    Global semantics guide the phase structure
    HBES model architecture
    ESA model architecture
    Deformable convolution diagram
    Schematic diagram of the difference between standard convolution and deformable convolution
    Contextual attention mechanism
    Height matrix distribution hot diagram
    DCT transformation schematic diagram
    Analog laser chart
    Laser images that have been transformed by imitation transformation
    Different complex natural backgrounds and typical target photos in different directions
    The qualitative evaluation results on TRT dataset. (a) Interference image;(b) Label image; (c) PatchMatch;(d) CoordFill;(e) Pconv; (f) Proposed
    Local detail amplification comparison image
    Ablation experiment. (a) Laser interference image; (b) Label image; (c) m/HBES; (d) m/Deformable Conv; (e) m/DCT; (f) Proposed
    The local zoomed-in comparison between m/DCT and the proposed algorithm
    • Table 1. TRT image data set distribution

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      Table 1. TRT image data set distribution

      Typical targetTraining setTest set
      Tank3200800
      Robot3200800
      Airplane1400350
      Ship1800450
    • Table 2. Experimental environmental parameter

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      Table 2. Experimental environmental parameter

      EnvironmentWindows11
      CPUAMD Ryzen 9 7900 X 12-Core Processor
      GPUNVIDIA GeForce RTX 3090
      Internal memory128 GB
      Development environmentPython3.9
      Deep learning frameworkPytorch
      CUDA version10.2
    • Table 3. Quantitative comparison results on TRT datasets

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      Table 3. Quantitative comparison results on TRT datasets

      AlgorithmSSIMPSNRL1
      Pconv0.97329.140.0026
      CoordFill0.65421.540.066
      PatchMatch0.64323.770.038
      Proposed0.98731.740.023
    • Table 4. The SSIM of the laser interference region on the TRT

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      Table 4. The SSIM of the laser interference region on the TRT

      AlgorithmInterference areaNon-interference area
      Pconv0.9570.992
      CoordFill0.7110.982
      PatchMatch0.6870.980
      Proposed0.9690.997
    • Table 5. The model computes complexity data

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      Table 5. The model computes complexity data

      AlgorithmParameter quantity/106GPU occupancy/GBInference time/s
      Pconv25.782.10.0769
      CoordFill34.401.40.0952
      PatchMatch5.81.20.578
      Proposed47.532.70.0477
    • Table 6. Ablation experiments of important components of the model in this paper

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      Table 6. Ablation experiments of important components of the model in this paper

      Variant modelHBESDerformDCTSSIMPSNRL1
      m/HBES-0.97130.370.026
      m/Derform-0.97930.470.023
      m/DCT-0.98130.880.025
      Full model0.98432.200.022
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    Peiyao ZHAO, Bin FENG, Xinpeng YANG, Xikui MIAO, Yunlong WU, Qing YE. Laser interference image inpainting with global semantic perception and texture frequency domain constraints[J]. Infrared and Laser Engineering, 2025, 54(5): 20240569

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    Paper Information

    Category: Optical imaging, display and information processing

    Received: Jan. 12, 2025

    Accepted: --

    Published Online: May. 26, 2025

    The Author Email:

    DOI:10.3788/IRLA20240569

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