• Infrared Technology
  • Vol. 45, Issue 9, 897 (2023)
Yu SHEN, Li LIANG, Hailong WANG, Yuan YAN, Guanghui LIU, and Jing SONG
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  • [in Chinese]
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    DOI: Cite this Article
    SHEN Yu, LIANG Li, WANG Hailong, YAN Yuan, LIU Guanghui, SONG Jing. Infrared and Visible Image Fusion Based on N-RGAN Model[J]. Infrared Technology, 2023, 45(9): 897 Copy Citation Text show less
    References

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    [2] HE G Q, JI J Q, DONG D D, et al. Infrared and visible image fusion method by using hybrid representation learning[J]. IEEE Geosci. Remote Sensing Lett, 2019, 16(11): 1796-1800.

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    [21] YANG Z, ZENG S. TPFusion: Texture preserving fusion of infrared and visible images via dense networks[J]. Entropy, 2022, 24(2): 294.

    [22] JING L, HUO H T, CHANG L, et al. AttentionFGAN: infrared and visible image fusion using attention-based generative adversarial networks[J]. IEEE Transactions on Multimedia, 2020, 23: 1383-1396.

    [25] MA J Y, XU H, JIANG J J, et al. DDcGAN: a dual-discriminator conditional generative adversarial network for multi-resolution image fusion[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 2020, 29: 4980-4995.

    [27] YU L, LIU S, WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24: 147-164.

    [28] YU L, XUN C, Ward R K, et al. Image fusion with convolutional sparse representation[J]. IEEE Signal Processing Letters, 2016, 23(12): 1882-1886.

    SHEN Yu, LIANG Li, WANG Hailong, YAN Yuan, LIU Guanghui, SONG Jing. Infrared and Visible Image Fusion Based on N-RGAN Model[J]. Infrared Technology, 2023, 45(9): 897
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