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基于注意力机制与光照感知网络的红外与可见光图像融合

杨艳春 闫岩 王可

杨艳春, 闫岩, 王可. 基于注意力机制与光照感知网络的红外与可见光图像融合[J]. 西南交通大学学报, 2024, 59(5): 1204-1214. doi: 10.3969/j.issn.0258-2724.20230529
引用本文: 杨艳春, 闫岩, 王可. 基于注意力机制与光照感知网络的红外与可见光图像融合[J]. 西南交通大学学报, 2024, 59(5): 1204-1214. doi: 10.3969/j.issn.0258-2724.20230529
YANG Yanchun, YAN Yan, WANG Ke. Infrared and Visible Image Fusion Based on Attention Mechanism and Illumination-Aware Network[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1204-1214. doi: 10.3969/j.issn.0258-2724.20230529
Citation: YANG Yanchun, YAN Yan, WANG Ke. Infrared and Visible Image Fusion Based on Attention Mechanism and Illumination-Aware Network[J]. Journal of Southwest Jiaotong University, 2024, 59(5): 1204-1214. doi: 10.3969/j.issn.0258-2724.20230529

基于注意力机制与光照感知网络的红外与可见光图像融合

doi: 10.3969/j.issn.0258-2724.20230529
基金项目: 长江学者和创新团队发展计划(IRT_16R36);国家自然科学基金项目(62067006);甘肃省科技计划(18JR3RA104);甘肃省高等学校产业支撑计划(2020C-19);甘肃省教育厅青年博士基金项目(2022QB-067);甘肃省自然科学基金项目(23JRRA847,21JR7RA300)
详细信息
    作者简介:

    杨艳春(1979—),女,副教授,博士,研究方向为图像处理、智能信息处理、机器学习,E-mail:yangyanchun102@sina.com

  • 中图分类号: TP391

Infrared and Visible Image Fusion Based on Attention Mechanism and Illumination-Aware Network

  • 摘要:

    部分图像融合方法未充分考虑图像环境的光照状况,导致融合图像中出现红外目标亮度不足以及整体画面亮度较低,从而影响纹理细节的清晰度. 为解决上述问题,提出一种基于注意力机制与光照感知网络相结合的红外与可见光图像融合算法. 首先,在训练融合网络之前利用光照感知网络计算当前场景是日间或夜间的概率,将其运用至融合网络损失函数中,用以指导融合网络训练;然后,在网络的特征提取部分采用空间注意力机制和深度可分离卷积对源图像进行特征提取,得到空间显著信息后,输入卷积神经网络(CNN)以提取深度特征;最后,将深度特征信息进行拼接用于图像重建,进而得到最终的融合图像. 实验结果表明:本文方法的互信息(MI)、视觉保真度(VIF)、平均梯度(AG)、融合质量(Qabf)与空间频率(SF)较对比方法分别平均提高39.33%、11.29%、26.27%、47.11%和39.01%;融合后的图像能够有效保留红外目标亮度,且包含丰富的纹理细节信息.

     

  • 图 1  空间注意力

    Figure 1.  Spatial attention

    图 2  深度可分离卷积

    Figure 2.  Depthwise separable convolution

    图 3  光照感知网络

    Figure 3.  Illumination-aware network

    图 4  融合网络结构

    Figure 4.  Fusion network structure

    图 5  指标对比折线图

    Figure 5.  Indicator comparison line chart

    表  1  特征提取部分卷积核大小与输出通道数

    Table  1.   Convolution kernel size and output channels in feature extraction part

    卷积层卷积核大小输出通道数/个
    Conv1-11 × 116
    Conv1-23 × 316
    Conv1-33 × 332
    Conv1-43 × 364
    Conv1-53 × 3128
    下载: 导出CSV

    表  2  图像重建部分卷积核大小与输出通道数

    Table  2.   Convolution kernel size and output channels in image reconstruction part

    卷积层卷积核大小输出通道数/个
    Conv2-13 × 3256
    Conv2-23 × 3128
    Conv2-33 × 364
    Conv2-43 × 332
    Conv2-51 × 11
    下载: 导出CSV

    表  3  实验结果

    Table  3.   Experimental results

    下载: 导出CSV

    表  4  红外目标分析

    Table  4.   Infrared target analysis

    下载: 导出CSV

    表  5  客观评价指标对比

    Table  5.   Comparison of objective evaluation indicators

    融合方法 MI VIF AG Qabf EN SF
    DenseFuse 2.3019 0.8175 3.5600 0.4457 6.8912 0.0352
    FusionGAN 2.3352 0.6541 2.4211 0.2341 6.5580 0.0246
    PMGI 2.3521 0.8692 3.5981 0.4117 7.0180 0.0344
    RFN-Nest 2.1184 0.8183 2.6693 0.3341 6.9632 0.0230
    SDNet 2.2606 0.7592 4.6117 0.4294 6.6948 0.0457
    U2Fusion 2.0102 0.8197 5.0233 0.4263 6.9967 0.0465
    DIVFusion 2.2226 0.9005 5.5595 0.3117 7.5932 0.0465
    PSFusion 2.3082 0.9000 5.5979 0.5223 7.2529 0.0478
    本文方法 3.1231 0.9008 4.7888 0.5578 6.8794 0.0489
    下载: 导出CSV

    表  6  消融实验对比

    Table  6.   Comparison of ablation experiments

    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-10-16
  • 修回日期:  2024-01-19
  • 网络出版日期:  2024-10-22
  • 刊出日期:  2024-01-30

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