Infrared Technology, Volume. 47, Issue 3, 367(2025)

An Improved Dual Discriminator Generative Adversarial Network Algorithm for Infrared and Visible Image Fusion

Guangfeng LIAO1, Zhiwei GUAN1,2, and Qiang CHEN1,3、*
Author Affiliations
  • 1School of Automobile and Transportation, Tianjin University of Technology and Education, Tianjin 300222, China
  • 2School of Automobile and Rail Transportation, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China
  • 3National & Local Joint Engineering Research Center for Intelligent Vehicle Road Collaboration and Safety Technology, Tianjin 300222, China
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    References(20)

    [1] [1] ZHANG H, XU H, TIAN X, et al. Image fusion meets deep learning: a survey and perspective[J]. Information Fusion, 2021, 76: 323-336.

    [3] [3] JIAN L, YANG X, LIU Z, et al. SEDRFuse: A symmetric encoder–decoder with residual block network for infrared and visible image fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-15.

    [4] [4] TANG L, YUAN J, ZHANG H, et al. PIAFusion: a progressive infrared and visible image fusion network based on illumination aware[J]. Information Fusion, 2022, 83: 79-92.

    [5] [5] MA J, TANG L, XU M, et al. STDFusionNet: an infrared and visible image fusion network based on salient target detection[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-13.

    [6] [6] Goodfellow I, Pouget Abadie J, Mirza M, et al. Generative adversarial nets[J/OL]. Advances in Neural Information Processing Systems, 2014: 2672-2680, https://arxiv.org/abs/1406.2661.

    [7] [7] LIU J, FAN X, HUANG Z, et al. Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 5802-5811.

    [8] [8] RAO D, XU T, WU X J. TGFuse: An infrared and visible image fusion approach based on transformer and generative adversarial network[J/OL]. IEEE Transactions on Image Processing, 2023, Doi: 10.1109/TIP.2023.3273451.

    [9] [9] HUANG Z, WANG X, HUANG L, et al. Ccnet: Criss-cross attention for semantic segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 603-612.

    [10] [10] ZHAO H, KONG X, HE J, et al. Efficient image super-resolution using pixel attention[C]//Computer Vision–ECCV, 2020: 56-72.

    [11] [11] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention–MICCAI, 2015: 234-241.

    [12] [12] Sandler M, Howard A, ZHU M, et al. Mobilenetv2: Inverted residuals and linear bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 4510-4520.

    [13] [13] ZHANG Y, TIAN Y, KONG Y, et al. Residual dense network for image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 2472-2481.

    [14] [14] SHI W, Caballero J, Huszr F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1874-1883.

    [15] [15] QIN X, ZHANG Z, HUANG C, et al. U2-Net: Going deeper with nested U-structure for salient object detection[J]. Pattern Recognition, 2020, 106: 107404.

    [16] [16] MA J, XU H, JIANG J, et al. DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion[J]. IEEE Transactions on Image Processing, 2020, 29: 4980-4995.

    [17] [17] LI H, WU X J. DenseFuse: A fusion approach to infrared and visible images[J]. IEEE Transactions on Image Processing, 2018, 28(5): 2614-2623.

    [18] [18] LI H, XU T, WU X J, et al. Lrrnet: A novel representation learning guided fusion network for infrared and visible images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(9): 11040-11052.

    [19] [19] LI H, WU X J, Durrani T. NestFuse: An infrared and visible image fusion architecture based on nest connection and spatial/channel attention models[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(12): 9645-9656.

    [20] [20] LI H, WU X J, Kittler J. RFN-Nest: An end-to-end residual fusion network for infrared and visible images[J]. Information Fusion, 2021, 73: 72-86.

    [21] [21] TANG L, YUAN J, MA J. Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network[J]. Information Fusion, 2022, 82: 28-42.

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    LIAO Guangfeng, GUAN Zhiwei, CHEN Qiang. An Improved Dual Discriminator Generative Adversarial Network Algorithm for Infrared and Visible Image Fusion[J]. Infrared Technology, 2025, 47(3): 367

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    Paper Information

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    Received: May. 19, 2024

    Accepted: Apr. 18, 2025

    Published Online: Apr. 18, 2025

    The Author Email: CHEN Qiang (chen@tute.edu.cn)

    DOI:

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