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Volume 45 Issue 7
Jul.  2023
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LONG Zhiliang, DENG Yueming, WANG Runmin, DONG Jun. Infrared and Visible Image Fusion Based on Saliency Detection and Latent Low-Rank Representation[J]. Infrared Technology , 2023, 45(7): 705-713.
Citation: LONG Zhiliang, DENG Yueming, WANG Runmin, DONG Jun. Infrared and Visible Image Fusion Based on Saliency Detection and Latent Low-Rank Representation[J]. Infrared Technology , 2023, 45(7): 705-713.
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Infrared and Visible Image Fusion Based on Saliency Detection and Latent Low-Rank Representation

  • School of Information Science and Engineering, Hunan Normal University, Changsha 410081, China

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  • Received Date: December 16, 2022
  • Revised Date: February 20, 2023
    Graphical Abstract
    • Abstract
  • To address the problems of missing detail and low contrast in the fusion of infrared and visible images, this study proposes a fusion method based on saliency detection and latent low-rank representation. First, a pre-fusion image is obtained by saliency detection for the infrared and visible images. Then, the infrared, visible, and pre-fused images are decomposed into low-rank and detail layers by the multilevel latent low-rank representation method. The detail layer is fused by combining the hyperspherical L2 norm and structural similarities, while the low-rank layer is fused using an approach based on the energy property. The final fused image is obtained by adding the fusion results of the low-rank and detail layers. The proposed method is compared with 11 representative image fusion methods by conducting subjective and objective evaluations of multiple groups of fused images. The results show that the image fusion method enhances the effective detail information and improves the image contrast, yielding a fusion result that is more in line with people's visual understanding.
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    • References (24)
  • [1]
    MA J, TANG L, XU M, et al. STD FusionNet: An infrared and visible image fusion network based on salient target detection[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13.
    [2]
    MA J, MA Y, LI C. 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
    [3]
    YAN H, ZHANG J X, ZHANG X. Injected infrared and visible image fusion via l1 decomposition model and guided filtering[J]. IEEE Transactions on Computational Imaging, 2022, 8: 162-173. DOI: 10.1109/TCI.2022.3151472
    [4]
    唐霖峰, 张浩, 徐涵, 等. 基于深度学习的图像融合方法综述[J]. 中国图象图形学报, 2023, 28(1): 3-36. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202301002.htm

    TANG L F, ZHANG H, XU H, et al. Deep learning-based image fusion: a survey[J]. Journal of Image and Graphics, 2023, 28(1): 3-36. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202301002.htm
    [5]
    ZHANG X. Deep learning-based multi-focus image fusion: A survey and a comparative study[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(9): 4819-4838.
    [6]
    REN L, PAN Z, CAO J, et al. Infrared and visible image fusion based on weighted variance guided filter and image contrast enhancement[J]. Infrared Physics & Technology, 2021, 114: 103662.
    [7]
    LI G, LIN Y, QU X. An infrared and visible image fusion method based on multi-scale transformation and norm optimization[J]. Information Fusion, 2021, 71: 109-129. DOI: 10.1016/j.inffus.2021.02.008
    [8]
    TAN X, GUO L. Visible and infrared image fusion based on visual saliency detection[C]//2020 19th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2020: 134-137.
    [9]
    Bavirisetti D P, Dhuli R. Two-scale image fusion of visible and infrared images using Sali ency detection[J]. Infrared Physics & Technology, 2016, 76: 52-64.
    [10]
    霍星, 邹韵, 陈影, 等. 双尺度分解和显著性分析相结合的红外与可见光图像融合[J]. 中国图象图形学报, 2021, 26(12): 2813-2825. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202112005.htm

    HUO X, ZOU Y, CHEN Y, et al. Dual-scale decomposition and saliency analysis based infrared and visible image fusion[J]. Journal of Image and Graphics, 2021, 26(12): 2813-2825 https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202112005.htm
    [11]
    LIU G, YAN S. Latent low-rank representation for subspace segmentation and feature extraction[C]//2011 International Conference on Computer Vision. IEEE, 2011: 1615-1622.
    [12]
    孙彬, 诸葛吴为, 高云翔, 等. 基于潜在低秩表示的红外和可见光图像融合[J]. 红外技术, 2022, 44(8): 853-862. http://hwjs.nvir.cn/article/id/7fc3a60d-61bb-454f-ad00-e925eeb54576

    SUN B, ZHUGE W W, GAO Y X, et al. Infrared and visible image fusion based on latent low-rank representation[J]. Infrared Technology, 2022, 44(8): 853-862. http://hwjs.nvir.cn/article/id/7fc3a60d-61bb-454f-ad00-e925eeb54576
    [13]
    LI H, WU X J, Kittler J. MDLatLRR: A novel decomposition method for infrared and visible image fusion[J]. IEEE Transactions on Image Processing, 2020, 29: 4733-4746.
    [14]
    TAN W, Tiwari P, Pandey H M, et al. Multimodal medical image fusion algorithm in the era of big data[J/OL]. Neural Computing and Applications, 2020, https://doi.org/10.1007/s00521-020-05173-2.
    [15]
    WANG Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600–612.
    [16]
    张蕾, 金龙旭, 韩双丽, 等. 采用非采样Contourlet变换与区域分类的红外和可见光图像融合[J]. 光学精密工程, 2015, 23(3): 810-818. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201503027.htm

    Zhang L, Jing L X, Han S L, et al. Fusion of infrared and visual images based on non-sampled Contourlet transform and region classification[J]. Optics and Precision Engineering, 2015, 23(3): 810-818. https://www.cnki.com.cn/Article/CJFDTOTAL-GXJM201503027.htm
    [17]
    Alexander Toet. TNO Image Fusion Dataset[EB/OL]. 2014. https://figshare.com/articles/dataset/TNO_Image_Fusion_Dataset/1008029.
    [18]
    Shreyamsha Kumar B K. Image fusion based on pixel significance using cross bilateral filter[J]. Signal, Image and Video Processing, 2015, 9(5): 1193-1204.
    [19]
    LIU Y, CHEN X, CHENG J, et al. Infrared and visible image fusion with convolutional neural networks[J]. International Journal of Wavelets, Multiresolution and Information Processing, 2018, 16(3): 1850018.
    [20]
    ZHOU Z, DONG M, XIE X, et al. Fusion of infrared and visible images for night-vision context enhancement[J]. Applied Optics, 2016, 55(23): 6480-6490.
    [21]
    LI S, KANG X, HU J. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing, 2013, 22(7): 2864-2875.
    [22]
    Burt P J, Adelson E H. The laplacian pyramid as a compact image code[J]. IEEE Transactions on Communications, 1983, 31(4): 532-540.
    [23]
    MA J, ZHOU Z, WANG B, et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization[J]. Infrared Physics & Technology, 2017, 82: 8-17.
    [24]
    ZHANG X, YE P, XIAO G. VIFB: A visible and infrared image fusion benchmark[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020: 104-105.
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