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2026 , Vol. 31 >Issue 2: 458 - 474

DOI: https://doi.org/10.1007/s12204-024-2735-y

Automation & Computer Technologies

Global Dense Two-Branch Cascade Network for Underwater Image Enhancement

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  • School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

Received date: 2023-05-19

  Accepted date: 2023-08-14

  Online published: 2024-05-06

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Abstract

In recent years, underwater image enhancement techniques has received a wide range of attention from related researchers with the rise of marine resource exploitation. As the existing network feature extraction is not sufficient and the enhancement results have the problems of incomplete defogging and inaccurate color bias correction, in this paper, an underwater image enhancement method based on global dense two-branch cascade network and spatial domain grayscale transformation is proposed. The global dense two-branch cascade network can amplify the global dimensional interaction features while reducing information reduction on the one hand, and extract spatial features by obtaining spatial information at different scales to achieve richer feature extraction on the other hand; the spatial domain grayscale transformation operation can improve the contrast while color correcting the image, which makes the image visual effect better. After the training is completed, an end-to-end inference can be performed on the underwater images. The experimental results show that this paper’s model works best on the EUVP dataset, and compared with the second best, this paper’s model obtains 3.371, 0.06, 0.716, 0.024, and 1.727 improvements in PSNR, SSIM, UIQM, UCIQE, and CCF, respectively. Compared with other representative methods, the proposed network achieves significant visual enhancement in dealing with severe color bias, low light, and detail loss in underwater images.

Cite this article

Wang Yan, Wang Likang, Zhang Jinfeng, Fan Xianghui . Global Dense Two-Branch Cascade Network for Underwater Image Enhancement[J]. Journal of Shanghai Jiaotong University(Science), 2026 , 31(2) : 458 -474 . DOI: 10.1007/s12204-024-2735-y

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