• Acta Photonica Sinica
  • Vol. 51, Issue 4, 0410002 (2022)
Zhishe WANG1、*, Wenyu SHAO1, Fengbao YANG2, and Yanlin CHEN1
Author Affiliations
  • 1School of Applied Science,Taiyuan University of Science and Technology,Taiyuan 030024,China
  • 2School of Information and Communication Engineering,North University of China,Taiyuan 030051,China
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    DOI: 10.3788/gzxb20225104.0410002 Cite this Article
    Zhishe WANG, Wenyu SHAO, Fengbao YANG, Yanlin CHEN. Infrared and Visible Image Fusion Method via Interactive Attention-based Generative Adversarial Network[J]. Acta Photonica Sinica, 2022, 51(4): 0410002 Copy Citation Text show less
    References

    [1] Xingchen ZHANG, Ping YE, H LEUNG et al. Object fusion tracking based on visible and infrared images: a comprehensive review. Information Fusion, 63, 166-187(2020).

    [2] Zhengzheng TU, Zhun LI, Chenglong LI et al. Multi-interactive dual-decoder for RGB-thermal salient object detection. IEEE Transactions on Image Processing, 30, 5678-5691(2021).

    [3] Zhanxiang FENG, Jianhuang LAI, Xiaohua XIE. Learning modality-specific representations for visible-infrared person reidentification. IEEE Transactions on Image Processing, 29, 579-590(2020).

    [4] Zhishe WANG, Jiawei XU, Xiaolin JIANG et al. Infrared and visible image fusion via hybrid decomposition of NSCT and morphological sequential toggle operator. Optik, 201, 163497(2020).

    [5] Zetao JIANG, Qi JIANG, Yongsong HUANG et al. Infrared and low-light-level visible light enhancement image fusion method based on latent low-rank representation and composite filtering. Acta Photonica Sinica, 49, 0410001(2020).

    [6] Jinlei MA, Zhiqiang ZHOU, Bo WANG et al. Infrared and visible image fusion based on visual saliency map and weighted least square optimization. Infrared Physics & Technology, 82, 8-17(2017).

    [7] Weiwei KONG, Yang LEI, Huaixun ZHAO. Adaptive fusion method of visible light and infrared images based on non-subsampled shearlet transform and fast non-negative matrix factorization. Infrared Physics & Technology, 67, 161-172(2014).

    [8] Sheng LV, Fengbao YANG, Linna JI et al. Combination fusion of multi-types mimic variables of infrared intensity and polarization image. Infrared and Laser Engineering, 47, 504005(2018).

    [9] Hang ZHANG, Han XU, Xin TIAN et al. Image fusion meets deep learning: a survey and perspective. Information Fusion, 76, 323-336(2021).

    [10] Hui LI, Xiaojun WU. DenseFuse: a fusion approach to infrared and visible images. IEEE Transactions on Image Processing, 28, 2614-2623(2019).

    [11] Yu ZHANG, Yu LIU, Peng SUN et al. IFCNN: a general image fusion framework based on convolutional neural network. Information Fusion, 54, 99-118(2020).

    [12] Lihua JIAN, Xiaomin YANG, Zheng LIU et al. SEDRFuse: A symmetric encoder-decoder with residual block network for infrared and visible image fusion. IEEE Transactions on Instrumentation and Measurement, 70, 1-15(2021).

    [13] Zhishe WANG, Junyao WANG, Yuanyuan WU et al. UNFusion: a unified multi-scale densely connected network for infrared and visible image fusion. IEEE Transactions on Circuits and Systems for Video Technology, 3109895(2021).

    [14] Hui LI, Xiaojun WU, J KITTLERB. RFN-Nest: an end-to-end residual fusion network for infrared and visible images. Information Fusion, 73, 72-86(2021).

    [15] Han XU, Jiayi MA, Junjun JIANG et al. U2fusion: a unified unsupervised image fusion network. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 502-518(2020).

    [16] Hao ZHANG, Han XU, Yang XIAO et al. Rethinking the image fusion: a fast unified image fusion network based on proportional maintenance of gradient and intensity, 34, 12797-12804(2020).

    [17] Yongzhi LONG, Haitao JIA, Yida ZHONG et al. RXDNFuse: a aggregated residual dense network for infrared and visible image fusion. Information Fusion, 69, 128-141(2021).

    [18] Jiayi MA, Wei YU, Pengwei LIANG et al. FusionGAN: a generative adversarial network for infrared and visible image fusion. Information Fusion, 48, 11-26(2019).

    [19] Jiayi MA, Hang ZHANG, Zhenfeng SHAO et al. GANMcC: a generative adversarial network with multiclassification constraints for infrared and visible image fusion. IEEE Transactions on Instrumentation and Measurement, 70, 1-14(2021).

    [20] Lili TANG, Gang LIU, Gang XIAO. Infrared and visible image fusion method based on dual-path cascade adversarial mechanism. Acta Photonica Sinica, 50, 0910004(2021).

    [21] Jiayi MA, Han XU, Junjun JIANG et al. DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion. IEEE Transactions on Image Processing, 29, 4980-4995(2020).

    [22] Yu FU, Xiaojun WU, T DURRANI. Image fusion based on generative adversarial network consistent with perception. Information Fusion, 72, 110-125(2021).

    [23] A TOET. TNO image fusion dataset. https://figshare.com/articles/TNImageFusionDataset/1008029

    [24] Han XU. Roadscene database. https://github.com/hanna-xu/RoadScene

    Zhishe WANG, Wenyu SHAO, Fengbao YANG, Yanlin CHEN. Infrared and Visible Image Fusion Method via Interactive Attention-based Generative Adversarial Network[J]. Acta Photonica Sinica, 2022, 51(4): 0410002
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