• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Attention-based for Multiscale Fusion Underwater Image Enhancement


Abstract

Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering. To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP). The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement. The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images. The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information. The detail information is protected by real-time superposition with feature information. Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics.


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Cite this article

[IEEE Style]
Z. Huang, J. Li and Z. Hua, "Attention-based for Multiscale Fusion Underwater Image Enhancement," KSII Transactions on Internet and Information Systems, vol. 16, no. 2, pp. 544-564, 2022. DOI: 10.3837/tiis.2022.02.010.

[ACM Style]
Zhixiong Huang, Jinjiang Li, and Zhen Hua. 2022. Attention-based for Multiscale Fusion Underwater Image Enhancement. KSII Transactions on Internet and Information Systems, 16, 2, (2022), 544-564. DOI: 10.3837/tiis.2022.02.010.

[BibTeX Style]
@article{tiis:25307, title="Attention-based for Multiscale Fusion Underwater Image Enhancement", author="Zhixiong Huang and Jinjiang Li and Zhen Hua and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.02.010}, volume={16}, number={2}, year="2022", month={February}, pages={544-564}}