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Acta Photonica Sinica, Volume. 51, Issue 4, 0410004(2022)

Multi-exposure Image Fusion Based on Attention Mechanism

Bendu BAI and Junpeng LI*
Author Affiliations
  • School of Communication and Information Engineering，Xi'an University of Posts & Telecommunications，Xi'an 710121，China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (14)
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    References (19)
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    Figures & Tables(14)
    Network structure of AMEFNet

    Fig. 1. Network structure of AMEFNet

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    Feature extraction network structure of AMEFNet

    Fig. 2. Feature extraction network structure of AMEFNet

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    Attention network structure of AMEFNet

    Fig. 3. Attention network structure of AMEFNet

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    Feature reconstruction network structure of Unet

    Fig. 4. Feature reconstruction network structure of Unet

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    Loss function curve of AMEFNet

    Fig. 5. Loss function curve of AMEFNet

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    Comparison of House sequence algorithm results

    Fig. 6. Comparison of House sequence algorithm results

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    Comparison of TableLamp sequence algorithm results

    Fig. 7. Comparison of TableLamp sequence algorithm results

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    Comparison of running time

    Fig. 8. Comparison of running time

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    Exposure fusion results of Studio sequence

    Fig. 9. Exposure fusion results of Studio sequence

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    Comparison of running time

    Fig. 10. Comparison of running time

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    • Table 1. The values of five quality metrics averaged over the fused images on test set

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      Table 1. The values of five quality metrics averaged over the fused images on test set

      Fusion MethodPSNRAGSFENVIF
      GFF58.224 05.623 418.826 97.393 90.825 3
      DSIFT59.825 65.105 117.103 87.354 30.759 1
      SPD-MEF58.754 55.877 321.033 97.039 90.794 9
      MEFNet58.380 46.029 320.596 67.394 80.844 9
      MEF-GAN58.773 64.756 513.554 86.982 30.621 1
      Ours59.115 06.199 121.098 67.203 70.874 6

    • Table 2. Time complexity of the different algorithm

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      Table 2. Time complexity of the different algorithm

      Fusion methodComplexityFLOPs（×109G）
      GFFO（Nmn）-
      DSIFTO（Nmn）-
      SPD-MEFO（Nmn）-
      MEFNet-13.2
      MEF-GAN-71.1
      Ours-165.6

    • Table 3. Evaluations of attention module on five quality metrics average

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      Table 3. Evaluations of attention module on five quality metrics average

      Fusion methodPSNRAGSFENVIF
      RAMEFNet58.750 95.986 620.135 16.98190.833 9
      AMEFNet59.115 06.199 121.098 67.203 70.874 6

    • Table 4. Evaluations of different hyperparameter λ on five quality metrics average

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      Table 4. Evaluations of different hyperparameter λ on five quality metrics average

      Fusion methodPSNRAGSFENVIF
      AMEFNet-λ0.0259.249 86.035 120.113 47.10110.843 1
      AMEFNet-λ0.259.115 06.199 121.098 67.203 70.874 6
      AMEFNet-λ259.09906.174 321.035 97.19150.879 5
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    Bendu BAI, Junpeng LI. Multi-exposure Image Fusion Based on Attention Mechanism[J]. Acta Photonica Sinica, 2022, 51(4): 0410004

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    Paper Information

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    Received: Nov. 3, 2021

    Accepted: Jan. 15, 2022

    Published Online: May. 18, 2022

    The Author Email: LI Junpeng (793348945@qq.com)

    DOI:10.3788/gzxb20225104.0410004

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology