Optics and Precision Engineering, Volume. 30, Issue 18, 2253(2022)

Infrared and visible image fusion based on multi-scale dense attention connection network

Yong CHEN*, Jiaojiao ZHANG, and Zhen WANG
Author Affiliations
  • School of Electronic and Information Engineering,Lanzhou Jiaotong University, Lanzhou730070,China
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    Figures & Tables(18)
    Overall network framework
    Multi-scale feature extraction diagram
    Dense connected module
    Convolutional block attention module
    Deformable Convolution
    Comparison of standard convolution and deformable convolution
    Channel attention module
    Spatial Attention Module
    Fusion strategy based on L1 norm
    Encoder subnet structure
    Comparison of fusion results of “street”
    Results of infrared and visible image fusion based on TNO dataset with different algorithms
    Comparison of six objective metrics for different algorithms
    Results of ablation experiments
    • Table 1. Objective evaluation of “street”

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      Table 1. Objective evaluation of “street”

      算法结构相似度空间频率信息熵视觉保真度差异相关系数边缘保持度
      CSR0.862 30.021 56.289 20.816 71.338 00.451 9
      DTCWT0.741 20.037 05.726 30.506 61.528 20.286 1
      NSCT0.654 00.031 25.832 00.472 41.672 10.278 8
      LatLRR0.571 20.028 16.677 60.580 11.506 20.382 7
      MDLatLRR0.572 10.047 07.015 30.829 01.754 20.367 2
      CNN0.861 20.040 96.562 70.702 71.623 50.329 0
      FusionGAN0.889 20.038 36.182 70.702 81.423 80.302 6
      DenseFuse0.635 10.028 16.827 30.582 01.532 90.320 4
      Ours0.905 70.049 87.301 40.818 21.827 10.418 2
    • Table 2. Average quantitative value of the fusion results of 10 groups

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      Table 2. Average quantitative value of the fusion results of 10 groups

      算法结构相似度空间频率信息熵视觉保真度差异相关系数边缘保持度
      CSR0.785 10.037 56.703 20.627 21.165 60.380 2
      DTCWT0.766 10.041 96.785 70.587 01.517 80.205 5
      NSCT0.632 20.037 06.628 00.528 11.475 90.329 1
      LatLRR0.723 00.032 96.621 70.628 31.589 20.313 1
      MDLatLRR0.730 20.053 07.091 60.672 11.452 30.319 3
      CNN0.826 60.047 66.211 30.618 11.487 10.327 6
      FusionGAN0.512 00.051 76.012 60.489 81.327 30.191 2
      DenseFuse0.821 70.037 66.932 80.713 21.610 10.272 3
      Ours0.893 00.060 17.104 20.758 71.800 40.370 3
    • Table 3. Objective indicators of ablation experiment

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      Table 3. Objective indicators of ablation experiment

      模块平均梯度结构相似性
      DC2.34270.7287
      M-DC2.68920.8021
      M-DC+CBAM3.46130.9261
      M-DC+D-CBAM3.73240.9637
    • Table 4. Computational amount and runtime

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      Table 4. Computational amount and runtime

      算法计算量/GFLOPs运行时间/s
      CNN2.7120.794
      FusionGAN3.3690.874
      DenseFuse3.3640.863
      Ours3.2830.851
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    Yong CHEN, Jiaojiao ZHANG, Zhen WANG. Infrared and visible image fusion based on multi-scale dense attention connection network[J]. Optics and Precision Engineering, 2022, 30(18): 2253

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    Paper Information

    Category: Information Sciences

    Received: Feb. 17, 2022

    Accepted: --

    Published Online: Oct. 27, 2022

    The Author Email: Yong CHEN (edukeylab@126.com)

    DOI:10.37188/OPE.20223018.2253

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