• Infrared and Laser Engineering
  • Vol. 53, Issue 8, 20240198 (2024)
Yan FAN1, Qiao LIU1, Di YUAN2, and Yunpeng LIU3
Author Affiliations
  • 1National Center for Applied Mathematics in Chongqing, Chongqing 401331, China
  • 2Guangzhou Institute of Technology, Xidian University, Guangzhou 710068, China
  • 3Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China
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    DOI: 10.3788/IRLA20240198 Cite this Article
    Yan FAN, Qiao LIU, Di YUAN, Yunpeng LIU. Spatial and frequency domain feature decoupling for infrared and visible image fusion[J]. Infrared and Laser Engineering, 2024, 53(8): 20240198 Copy Citation Text show less
    References

    [1] L LI, H WANG, C LI. A review of deep learning fusion methods for infrared and visible light images. Infrared and Laser Engineering, 51, 20220125-356(2022).

    [2] Y SHEN, C H HUANG, F HUANG. Research progress of infrared and visible image fusion technology. Infrared and Laser Engineering, 50, 20200467-169(2021).

    [3] M RASHID, M A KHAN, M ALHAISONI et al. A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection. Sustainability, 12, 5037(2020).

    [4] J HU, S LI. The multiscale directional bilateral filter and its application to multisensor image fusion. Information Fusion, 13, 196-206(2012).

    [5] B YANG, S LI. Pixel-level image fusion with simultaneous orthogonal matching pursuit. Information Fusion, 13, 10-19(2012).

    [6] D P BAVIRISETTI, R DHULI. Two-scale image fusion of visible and infrared images using saliency detection. Infrared Physics & Technology, 76, 52-64(2016).

    [7] H LI, X J WU. DenseFuse: A fusion approach to infrared and visible images. IEEE Transactions on Image Processing, 28, 2614-2623(2018).

    [8] H LI, X J WU, T DURRANI. NestFuse: An infrared and visible image fusion architecture based on nest connection and spatial/channel attention models. IEEE Transactions on Instrumentation and Measurement, 69, 9645-9656(2020).

    [9] H LI, X J WU, J KITTLER. RFN-Nest: An end-to-end residual fusion network for infrared and visible images. Information Fusion, 73, 72-86(2021).

    [10] L TANG, J YUAN, J MA. Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network. Information Fusion, 82, 28-42(2022).

    [11] H XU, X WANG, J MA. DRF: Disentangled representation for visible and infrared image fusion. IEEE Transactions on Instrumentation and Measurement, 70, 1-13(2021).

    [12] ZHAO Z, XU S, ZHANG C, et al. DIDFuse: Deep image decomposition f infrared visible image fusion [C]TwentyNinth International Joint Conference on Artificial Intelligence, 2020, 135: 970976.

    [13] H XU, M GONG, X TIAN et al. CUFD: An encoder–decoder network for visible and infrared image fusion based on common and unique feature decomposition. Computer Vision and Image Understanding, 218, 103407(2022).

    [14] L TANG, J YUAN, H ZHANG et al. PIAFusion: A progressive infrared and visible image fusion network based on illumination aware. Information Fusion, 83, 79-92(2022).

    [15] L TANG, X XIANG, H ZHANG et al. DIVFusion: Darkness-free infrared and visible image fusion. Information Fusion, 91, 477-493(2023).

    [16] ZAMIR S W, ARA A, KHAN S, et al. Restmer: Efficient transfmer f highresolution image restation [C]Proceedings of the IEEECVF Conference on Computer Vision Pattern Recognition, 2022: 57285739.

    [17] WU Z, LIU Z, LIN J, et al. Lite transfmer with longsht range attention [C]Proceedings of the International Conference on Learning Representations, 2020: 11886.

    [18] D P KINGMA, P DHARIWAL. Glow: Generative flow with invertible 1×1 convolutions. Advances in Neural Information Processing Systems, 10, 10236-10245(2018).

    [19] QIN Z, ZHANG P, WU F, et al. Fca: Frequency channel attention wks [C]Proceedings of the IEEECVF InterNational Conference on Computer Vision, 2021: 763772.

    [20] A TOET, M A HOGERVORST. Progress in color night vision. Optical Engineering, 51, 010901(2012).

    [21] XU H, MA J, LE Z, et al. Fusiondn: A unified densely connected wk f image fusion [C]Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 1248412491.

    [22] J MA, L TANG, F FAN et al. SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer. IEEE/CAA Journal of Automatica Sinica, 9, 1200-1217(2022).

    [23] H ZHANG, J MA. SDNet: A versatile squeeze-and-decomposition network for real-time image fusion. International Journal of Computer Vision, 129, 2761-2785(2021).

    [24] H XU, J MA, J JIANG et al. U2Fusion: A unified unsupervised image fusion network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 502-518(2020).

    [25] J MA, W YU, P LIANG et al. FusionGAN: A generative adversarial network for infrared and visible image fusion. Information Fusion, 48, 11-26(2019).

    [26] W TANG, F HE, Y LIU et al. DATFuse: Infrared and visible image fusion via dual attention transformer. IEEE Transactions on Circuits and Systems for Video Technology, 33, 3159-3172(2023).

    Yan FAN, Qiao LIU, Di YUAN, Yunpeng LIU. Spatial and frequency domain feature decoupling for infrared and visible image fusion[J]. Infrared and Laser Engineering, 2024, 53(8): 20240198
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