基于引导滤波二尺度分解的红外与可见光图像融合

张慧, 韩新宁, 韩惠丽, 常莉红

张慧, 韩新宁, 韩惠丽, 常莉红. 基于引导滤波二尺度分解的红外与可见光图像融合[J]. 红外技术, 2023, 45(11): 1216-1222.
引用本文: 张慧, 韩新宁, 韩惠丽, 常莉红. 基于引导滤波二尺度分解的红外与可见光图像融合[J]. 红外技术, 2023, 45(11): 1216-1222.
ZHANG Hui, HAN Xinning, HAN Huili, CHANG Lihong. Two-scale Image Fusion of Visible and Infrared Images Based on Guided Filtering Decomposition[J]. Infrared Technology , 2023, 45(11): 1216-1222.
Citation: ZHANG Hui, HAN Xinning, HAN Huili, CHANG Lihong. Two-scale Image Fusion of Visible and Infrared Images Based on Guided Filtering Decomposition[J]. Infrared Technology , 2023, 45(11): 1216-1222.

基于引导滤波二尺度分解的红外与可见光图像融合

基金项目: 

宁夏自然科学基金 2022AAC03331

宁夏自然科学基金 2021AAC03028

宁夏自然科学基金 2022AAC03300

宁夏自然科学基金 2023AAC03330

详细信息
    作者简介:

    张慧(1977-),女,宁夏固原人,硕士,教授,主要研究方向为图形图像处理。E-mail:2466437143@qq.com

  • 中图分类号: TN911.7

Two-scale Image Fusion of Visible and Infrared Images Based on Guided Filtering Decomposition

  • 摘要: 为了降低多尺度分解融合算法的复杂性,并提高融合图像适应人类视觉特点,本文提出一种基于引导滤波二尺度分解的红外与可见光图像融合的方法。首先利用引导滤波对可见光图像实施增强的图像预处理,然后利用引导滤波将源图像分解为基础层和细节层。在细节层的融合规则中我们采用能量保护和细节提取的方法,最后将融合后的细节层与基础层合成融合结果。实验结果表明所给方法在提高视觉感知、细节处理、边缘保护等方面都有良好的效果。本文最后还讨论了可见光图像增强对融合方法的影响:从实验数据可知,增强可以提升融合效果,但在图像融合中融合方法才是关键。
    Abstract: We proposed a two-scale image-fusion method for infrared and visible light image fusion based on guided filtering to reduce the complexity of multi-scale decomposition fusion algorithms and improve the adaptability of fused images to human visual characteristics. First, we used guided filtering to enhance the visible image and decomposed the source images into base and detail layers using guided filtering. In the fusion rules of the detail layer, we adopted the energy protection methods and detail extraction. Finally, we combined the fused detail layer with the base layer to synthesize the fusion results. The experimental results showed that the proposed method improves the visual effect, detail processing, and edge protection. We discussed the impact of visible image enhancement on fusion methods from experimental data. Enhancement can improve the fusion effect, but the fusion method is key in image fusion.
  • 图  1   基于引导滤波二尺度分解的图像融合流程

    Figure  1.   Flow chart of image fusion based on two scale decomposition with guided filtering

    图  2   测试集

    Figure  2.   Test set

    图  3   两组实验结果对比

    Figure  3.   Comparison of two experimental result graphs

    图  4   第一组实验结果放大图

    Figure  4.   Enlarged image of the first group of experimental results

    图  5   不同融合方法的效果对比

    Figure  5.   Comparison of renderings of different fusion methods

    表  1   用SR、PCNN、GFF、TSF、MGF和E_GF_TSF方法融合得到的指标

    Table  1   Comparison with SR, PCNN, GFF, TSF, MGF and E_GF_TSF of different processing results

    Criteria SR PCNN GFF TSF MGF E_GF_TSF
    EN 7.133011 6.698167 6.816344 6.754622 6.7379 7.174367
    SD 45.53829 35.5092 39.79169 37.83782 39.54778 45.63526
    MI 0.487656 0.352689 0.413833 0.397111 0.375622 0.658178
    QG 0.560078 0.498867 0.633811 0.521889 0.531589 0.5816
    PS 23.33091 21.3139 22.16608 20.9447 22.44031 25.3944
    Time/s 0.25619 1.324774 0.398338 0.074057 0.177425 0.35371
    下载: 导出CSV

    表  2   用TSF, MGF, GF_TSF, E_TSF, E_MGF和E_GF_TSF方法融合得到的指标

    Table  2   Comparison with TSF, MGF, GF_TSF, E_TSF, E_MGF and E_GF_TSF of different processing results

    Criteria TSF MGF GF_TSF E_TSF E_MGF E_GF_TSF
    EN 6.754622 6.7379 6.895756 7.122389 7.126056 7.174367
    SD 37.83782 39.54778 42.16097 42.47871 45.5259 45.63526
    MI 0.397111 0.375622 0.723633 0.3706 0.348256 0.658178
    QG 0.521889 0.531589 0.585978 0.540311 0.532933 0.5816
    PS 20.9447 22.44031 21.40484 25.36989 27.37799 25.3944
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-18
  • 修回日期:  2023-06-20
  • 刊出日期:  2023-11-19

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