LI Wei, TIAN Shishun, LIU Guangli, ZOU Wenbin. Structural Similarity Fusion of Infrared and Visible Image in the M-SWF Domain[J]. Infrared Technology , 2024, 46(3): 280-287.
Citation: LI Wei, TIAN Shishun, LIU Guangli, ZOU Wenbin. Structural Similarity Fusion of Infrared and Visible Image in the M-SWF Domain[J]. Infrared Technology , 2024, 46(3): 280-287.

Structural Similarity Fusion of Infrared and Visible Image in the M-SWF Domain

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  • Received Date: November 13, 2022
  • Revised Date: March 07, 2023
  • This study introduces a multiscale sliding window filter (M-SWF) image fusion method to address issues with traditional filter banks in infrared and visible image fusion. First, a multiscale image decomposition method based on SWF is proposed to extract the structural detail layers and base layers of the source image. Second, the L1 norm fusion rule (L1-Fusion, L1F) is used to integrate the structural detail layers, which can extract the structure of the image. Then, to highlight the salient objects, energy attribute fusion (EAF), which is a rule for fusing image energy contributions, is used to integrate the base layers, and the fusion results are obtained by stacking the integrated multiscale structure detail layers and base layers. The energy contribution coefficient was analyzed, and a suitable energy contribution coefficient was obtained for the fusion of infrared and visible images in the M-SWF domain from subjective and objective perspectives. Compared with other fusion methods, the M-SWF not only improves the ability to extract the structural information of the source image but also improves the poor fusion effect and effectively highlights salient targets by integrating the energy attributes of the image.
  • [1]
    MA Jiayi, MA Yong, LI Chang. Infrared and visible image fusion methods and applications: a survey [J]. Information Fusion, 2019, 45: 153-178. DOI: 10.1016/j.inffus.2018.02.004
    [2]
    ZHAO Z, XU S, ZHANG C, et al. Bayesian fusion for infrared and visible images[J]. Signal Processing, 2020, 177: 165-168. DOI: 10.1016/ j.sigpro.2020.107734
    [3]
    HUAN Kewei, LI Xiangyang, CAO Yutong, et al. Infrared and visible image fusion of convolutional neural network and NSST[J]. Infrared and Laser Engineering, 2022, 51(3): 20210139. DOI: 10.3788/IRLA20210139.
    [4]
    CHENG Boyang, LI Ting, WANG Yulin. Fusion of infrared and visible light images based on visual saliency weighting and maximum gradient singular value[J]. Chinese Optics, 2022, 15(4): 675-688. DOI: 10.37188/CO.2022-0124
    [5]
    李威, 李忠民. 一种基于EASSF的红外与可见光图像视觉保真度融合[J]. 红外技术, 2022, 44(7): 686-692. http://hwjs.nvir.cn/article/id/2c60451f-34b1-47a3-bab6-629774a73a0c

    LI Wei, LI Zhongmin. Visual fidelity fusion of infrared and visible image using edge-aware smoothing-sharpening filter[J]. Infrared Technology, 2022, 44(7): 686-692. http://hwjs.nvir.cn/article/id/2c60451f-34b1-47a3-bab6-629774a73a0c
    [6]
    李永萍, 杨艳春, 党建武, 等. 基于变换域VGGNet19的红外与可见光图像融合[J]. 红外技术, 2022, 44(12): 1293-1300. http://hwjs.nvir.cn/article/id/48843dec-a48b-468f-b743-4a00c345f406

    LI Yongping, YANG Yanchun, DANG Jianwu, et al. Infrared and visible image fusion based on transform domain VGGNet19[J]. Infrared Technology, 2022, 44(12): 1293-1300. http://hwjs.nvir.cn/article/id/48843dec-a48b-468f-b743-4a00c345f406
    [7]
    雷大江, 杜加浩, 张莉萍, 等. 联合多流融合和多尺度学习的卷积神经网络遥感图像融合方法[J]. 电子与信息学报, 2022, 44(1): 237-244. Doi: 10.11999/JEIT200792.

    LEI Dajiang, DU Jiahao, ZHANG Liping, et al. Multi-stream architecture and multi-scale convolutional neural network for remote sensing image fusion [J]. Journal of Electronics & Information Technology, 2022, 44(1): 237-244. Doi: 10.11999/JEIT200792.
    [8]
    马梁, 苟于涛, 雷涛, 等. 基于多尺度特征融合的遥感图像小目标检测[J]. 光电工程, 2022, 49(4): 49-65. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC202204005.htm

    MA Liang, GOU Yutao, LEI Tao, et al. Small object detection based on multi-scale feature fusion using remote sensing images[J]. Opto-Electron Eng, 2022, 49(4): 49-65. https://www.cnki.com.cn/Article/CJFDTOTAL-GDGC202204005.htm
    [9]
    钱金卓, 马骏, 李峰, 等. 面向CMOS遥感相机的多曝光图像融合方法[J]. 遥感信息, 2022, 37(4): 51-57. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX202204008.htm

    QIAN Jinzhuo, MA Jun, LI Feng, et al. Multi-exposure image fusion method for CMOS remote sensing camera [J]. Remote Sensing Information, 2022, 37(4): 51-57. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXX202204008.htm
    [10]
    LI Shutao, KANG Xudong, HU Jianwen. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2864-2875. Doi: 10.1109/TIP.2013.2244222.
    [11]
    Shreyamsha Kumar B K. Image fusion based on pixel significance using cross bilateral filter[J]. Signal Image Video Process, 2015, 9(5): 1193-1204. Doi: 10.1007/s11760-013-0556-9.
    [12]
    ZHAN K, XIE Yuange, MIN Yufang. Fast filtering image fusion[J]. J. Electron. Imaging, 2017, 26(6): 063004. Doi: 10.1117/1.JEI.26.6.063004.
    [13]
    LI Hui, WU Xiaojun, Tariq S Durrani. Infrared and visible image fusion with ResNet and zero-phase component analysis[J]. Infrared Physics & Technology, 2019, 102: 1030390. Doi: 10.1016/j.infrared.2019.-103039.
    [14]
    TAN Wei, Thitn W, XIANG P, et al. Multi-modal brain image fusion based on multi-level edge-preserving filtering[J]. Biomedical Signal Processing and Control, 2021, 64: 102280. Doi: 10.1016/j. bspc.2020.102280.
    [15]
    YIN Hui, GONG Yuanhao, QIU Guoping. Side window guided filtering[J]. Signal Process, 2019, 165: 315-330. Doi: 10.1016/j.sigpro. 2019.07.026.
    [16]
    LI Xiaosong, ZHOU Fuqiang, TAN Haishu, et al. Multimodal medical image fusion based on joint bilateral filter and local gradient energy[J]. Information Sciences, 2021, 569: 302-325.
    [17]
    LI Hui, WU Xiaojun. DenseFuse: a fusion approach to infrared and visible images [J]. IEEE Trans. Image Process, 2019, 28(5): 2614-2623. Doi: 10.1109/TIP.2018.2887342.
    [18]
    Toet A. TNO Image Fusion Dataset[EB/OL]. [2022-10-30]. http://figshare.com/articles/-TNO_Image_Fusion_Dataset/1008029.
    [19]
    Xydeas C S, Petrovic V. Objective image fusion performance measure [J]. Electron. Lett, 2000, 36(4): 308-309. Doi: 10.1109/ICCV.2005.175.
    [20]
    Aslantas V, Bendes E. A new image quality metric for image fusion: the sum of the correlations of differences[J]. AEU- Int. J. Electron. Commune, 2015, 69(12): 1890-1896. Doi: 10.1016/j.aeue.2015.09.004.
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