Electronics Optics & Control, Volume. 29, Issue 7, 43(2022)

Enhancement of Underwater Images Via Global Feature Dual Attention Fusion Generative Adversarial Network

LIU Xu1... LIN Sen2 and TAO Zhiyong1 |Show fewer author(s)
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  • 1[in Chinese]
  • 2[in Chinese]
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    Aiming at the problems of heterogeneous fog distribution and uneven illumination of underwater images, an image enhancement method is proposed based on global feature dual attention fusion generative adversarial network.Firstly, the convolutional layers are used instead of average pooling layers to continuously down-sample the input images and extract the global features.Secondly, the global feature dual attention fusion block is constructed, which is adaptive to the changing water environment and can enhance underwater information with various dissemination degrees more significantly.Finally, conditional information is incorporated as a restriction in training to increase the networks stability.The results of experiments demonstrate that the proposed algorithm outperforms the classic and latest algorithms and has fine functionality.

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    LIU Xu, LIN Sen, TAO Zhiyong. Enhancement of Underwater Images Via Global Feature Dual Attention Fusion Generative Adversarial Network[J]. Electronics Optics & Control, 2022, 29(7): 43

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    Paper Information

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    Received: Jun. 3, 2021

    Accepted: --

    Published Online: Aug. 1, 2022

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2022.07.008

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