• Acta Photonica Sinica
  • Vol. 52, Issue 11, 1110003 (2023)
Xin FENG, Jieming YANG*, Hongde ZHANG, and Guohang QIU
Author Affiliations
  • School of Mechanical Engineering,Key Laboratory of Manufacturing Equipment Mechanism Design and Controlof Chongqing,Chongqing Technology and Business University,Chongqing 400067,China
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    DOI: 10.3788/gzxb20235211.1110003 Cite this Article
    Xin FENG, Jieming YANG, Hongde ZHANG, Guohang QIU. Infrared and Visible Image Fusion Based on Dual Channel Residual Dense Network[J]. Acta Photonica Sinica, 2023, 52(11): 1110003 Copy Citation Text show less
    Auto-Encoder structure framework
    Fig. 1. Auto-Encoder structure framework
    Encoder network structure
    Fig. 2. Encoder network structure
    A Residual dense network fusion framework based on dual-channel cascade
    Fig. 3. A Residual dense network fusion framework based on dual-channel cascade
    Infrared and visible light fusion results of different channels
    Fig. 4. Infrared and visible light fusion results of different channels
    Infrared and visible light fusion results of different fusion strategies
    Fig. 5. Infrared and visible light fusion results of different fusion strategies
    Fusion results of“pedestrian”
    Fig. 6. Fusion results of“pedestrian”
    Infrared and visible light fusion results of each algorithm
    Fig. 7. Infrared and visible light fusion results of each algorithm
    Objective experimental line chart
    Fig. 8. Objective experimental line chart
    LayerSizeStrideInputOutputActivation
    EncoderConv31116ELU
    Dual path block×3-----
    DecoderResConv313216ELU
    DenConv316464-
    Conv316432-
    Conv313216ELU
    GeneralConv313216-
    Conv31161Tanh
    Dual path blockConv11Res:1632ELU
    Den:1632
    Conv31Res:3232ELU
    Den:3232
    Conv11Res:3232ELU
    Den:3232
    Table 1. The table of network structure
    MetricsDifferent channelsDifferent fusion strategies
    ResDenseDual PathAddAvgMaxDual path attention
    SSIM0.906 00.912 30.923 60.912 30.905 90.891 80.928 9
    SCD1.712 91.702 81.718 91.696 51.555 41.535 01.860 5
    PSNR63.601 165.391 865.682 859.621 664.248 662.230 566.072 7
    Table 2. The table of mean quantitative values of experimental evaluation indexes for ablation
    MetricsMethods
    MST-SRGTFIFCNNPMGIU2FusionDenseFuseRes2NetFusionGANOurs
    SD32.576 429.375 139.122 832.641 536.615 032.877 339.870 924.325 540.747 1
    CC0.577 80.437 10.585 30.561 60.598 30.610 20.558 60.493 50.604 5
    PSNR65.903 663.622 965.824 663.405 265.111 564.650 764.632 062.664 966.072 7
    MS-SSIM0.942 90.854 60.940 10.912 60.923 30.906 60.892 20.784 90.932 4
    SCD1.760 31.024 81.900 51.873 51.872 81.606 41.668 11.596 41.884 9
    MI1.879 32.398 92.413 62.139 32.178 62.438 72.823 22.070 43.123 8
    Table 3. The average quantitative value of each evaluation index
    Xin FENG, Jieming YANG, Hongde ZHANG, Guohang QIU. Infrared and Visible Image Fusion Based on Dual Channel Residual Dense Network[J]. Acta Photonica Sinica, 2023, 52(11): 1110003
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