Infrared and Laser Engineering, Volume. 51, Issue 8, 20210957(2022)

Parallel multifeature extracting network for infrared image enhancement

Zhongxiang Pang1, Xie Liu2, Guihua Liu1, Yinjun Gong1, Han Zhou2, and Hongwei Luo2
Author Affiliations
  • 1School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China
  • 2Shenzhen Launch Digital Technology Co., Ltd, Shenzhen 518000, China
  • show less
    Figures & Tables(14)
    Architecture of the overall network
    Module of the MS-feature extraction
    Architecture of attention block
    Architecture of decoder block
    Part of the training sample pairs
    Image enhancement on BSD200 with 条件下BSD200图像增强效果
    Test result on real infrared images with 条件下真实红外图像测试结果
    Image enhancement effect on BSD200 with different using proposed method在不同作用下文中方法在BSD200数据集的图像增强效果
    • Table 1. Parameters of SFW map blocks

      View table
      View in Article

      Table 1. Parameters of SFW map blocks

      TypeKspc
      Conv11032
      conv31132
      Conv51232
      Conv31132
    • Table 2. Parameters of TSFEB

      View table
      View in Article

      Table 2. Parameters of TSFEB

      PathTypeKspc
      2_1Conv32148
      2_2Conv11032
      2_3MS-FE///62
      2_4MS-FE///96
      2_5Conv31196
      3_1Conv44096
      3_2Conv11064
      3_3AB///64
      3_4Conv31264
      3_5Conv31264
      3_6Conv31264
      3_7AB///64
      3_8deconv42196
    • Table 3. Test result on BSD200 with \begin{document}${\boldsymbol{\alpha}} $\end{document}∈ [0.5, 0.51]

      View table
      View in Article

      Table 3. Test result on BSD200 with \begin{document}${\boldsymbol{\alpha}} $\end{document}∈ [0.5, 0.51]

      MethodHECLAHESSRMSRTENTIECNNIE-GANProposed
      PSNR15.9522.1916.5117.5725.0724.6026.2335.42
      SSIM0.720.930.880.900.820.800.920.95
    • Table 4. Test result on real infrared images with \begin{document}${\boldsymbol{\alpha}}$\end{document}∈ [0.5, 0.51]

      View table
      View in Article

      Table 4. Test result on real infrared images with \begin{document}${\boldsymbol{\alpha}}$\end{document}∈ [0.5, 0.51]

      MethodHECLAHESSRMSRTENTIECNNIE-GANProposed
      PSNR13.0624.7815.8918.3625.7723.2526.8535.72
      SSIM0.530.950.890.930.890.880.940.96
    • Table 5. PSNR and SSIM on BSD200 with different \begin{document}${\boldsymbol{\alpha}}$\end{document} using proposed method

      View table
      View in Article

      Table 5. PSNR and SSIM on BSD200 with different \begin{document}${\boldsymbol{\alpha}}$\end{document} using proposed method

      $ \alpha $$ \left[\mathrm{0.1,0.11}\right] $$ \left[\mathrm{0.2,0.21}\right] $$ \left[\mathrm{0.3,0.31}\right] $$ \left[\mathrm{0.4,0.41}\right] $
      PSNR30.576231.037028.129032.0594
      SSIM0.83430.88670.90740.9058
    • Table 6. Ablation experiments

      View table
      View in Article

      Table 6. Ablation experiments

      MethodPSNRSSIMTime/s
      Path1(SFW)27.350.920.05
      Path2 without MS-feature extraction25.260.830.09
      Path227.030.870.14
      Path324.580.920.14
      TSFEB29.150.900.21
      SFW + TSFEB without MS-feature extraction30.990.930.22
      SFW + TSFEB35.420.950.26
    Tools

    Get Citation

    Copy Citation Text

    Zhongxiang Pang, Xie Liu, Guihua Liu, Yinjun Gong, Han Zhou, Hongwei Luo. Parallel multifeature extracting network for infrared image enhancement[J]. Infrared and Laser Engineering, 2022, 51(8): 20210957

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Infrared technology and application

    Received: Dec. 13, 2021

    Accepted: --

    Published Online: Jan. 9, 2023

    The Author Email:

    DOI:10.3788/IRLA20210957

    Topics