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Volume 11 Issue 3
Mar.  2024

IEEE/CAA Journal of Automatica Sinica

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H. Liu, Q. Zhang, Y. Hu, H. Zeng, and  B. Fan,  “Unsupervised multi-expert learning model for underwater image enhancement,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 708–722, Mar. 2024. doi: 10.1109/JAS.2023.123771
Citation: H. Liu, Q. Zhang, Y. Hu, H. Zeng, and  B. Fan,  “Unsupervised multi-expert learning model for underwater image enhancement,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 708–722, Mar. 2024. doi: 10.1109/JAS.2023.123771

Unsupervised Multi-Expert Learning Model for Underwater Image Enhancement

doi: 10.1109/JAS.2023.123771
Funds:  This work was supported in part by the National Key Research and Development Program of China (2020YFB1313002), the National Natural Science Foundation of China (62276023, U22B2055, 62222302, U2013202), the Fundamental Research Funds for the Central Universities (FRF-TP-22-003C1), and the Postgraduate Education Reform Project of Henan Province (2021SJGLX260Y)
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  • Underwater image enhancement aims to restore a clean appearance and thus improves the quality of underwater degraded images. Current methods feed the whole image directly into the model for enhancement. However, they ignored that the R, G and B channels of underwater degraded images present varied degrees of degradation, due to the selective absorption for the light. To address this issue, we propose an unsupervised multi-expert learning model by considering the enhancement of each color channel. Specifically, an unsupervised architecture based on generative adversarial network is employed to alleviate the need for paired underwater images. Based on this, we design a generator, including a multi-expert encoder, a feature fusion module and a feature fusion-guided decoder, to generate the clear underwater image. Accordingly, a multi-expert discriminator is proposed to verify the authenticity of the R, G and B channels, respectively. In addition, content perceptual loss and edge loss are introduced into the loss function to further improve the content and details of the enhanced images. Extensive experiments on public datasets demonstrate that our method achieves more pleasing results in vision quality. Various metrics (PSNR, SSIM, UIQM and UCIQE) evaluated on our enhanced images have been improved obviously.

     

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    Highlights

    • An unsupervised framework is used to obviate the need for paired underwater images
    • Design a multi-expert model by considering channel differences of underwater images
    • Content perceptual loss and edge loss are introduced to preserve image details
    • The enhanced images improve the performance of underwater visual tasks significantly

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