Method of Vision-Task-Friendly Underwater Image Enhancement

CHENG Miao; WEI Yanhui; SUN Wenbin; HOU Tongtong

doi:10.11993/j.issn.2096-3920.2026-0023

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CHENG Miao, WEI Yanhui, SUN Wenbin, HOU Tongtong. Method of Vision-Task-Friendly Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0023

Citation:

CHENG Miao, WEI Yanhui, SUN Wenbin, HOU Tongtong. Method of Vision-Task-Friendly Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0023

CHENG Miao, WEI Yanhui, SUN Wenbin, HOU Tongtong. Method of Vision-Task-Friendly Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0023

Citation:

CHENG Miao, WEI Yanhui, SUN Wenbin, HOU Tongtong. Method of Vision-Task-Friendly Underwater Image Enhancement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0023

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Method of Vision-Task-Friendly Underwater Image Enhancement

doi: 10.11993/j.issn.2096-3920.2026-0023

CHENG Miao^1
,,
WEI Yanhui^{1, 2, 3
,
,},
SUN Wenbin^{2, 3
,},
HOU Tongtong^{2, 3
,}

1.
Sanya Nanhai Innovation and Development Base of Harbin Engineering University, Sanya 572024, China
2.
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
3.
Key Laboratory of Marine Intelligent Equipment and Instruments in Heilongjiang Province

Received Date: 2026-01-20
Accepted Date: 2026-03-09
Rev Recd Date: 2026-02-15

Available Online: 2026-03-30

Abstract

Abstract

Underwater images suffer from severe color and structural distortions, which degrade the performance of various underwater vision tasks. Existing underwater image enhancement methods focus on improving visual appearance while ignoring the necessity of optimizing the downstream vision tasks. To address this issue, this paper proposes a Visual Task-Friendly underwater image enhancement Network(VTF-Net). Specifically, we first design a novel Spatial-Frequency Fusion enhancement module(SFF), which can significantly improve the model’s perception of texture details and image fidelity. Second, to achieve efficient information transmission between the encoder and decoder, we introduce a Multi-Scale Cross-Attention module(MSCA) and a Bottleneck Attention module(BNA), which enhance the perception of global gradients while ensuring efficient feature extraction, thereby effectively alleviating color cast and blurring. Finally, in line with the concept of visual task-friendliness, we propose a detection loss function that guides the optimization direction of the model by incorporating underwater object detection results. Experimental results demonstrate that the proposed method achieves superior performance in both qualitative and quantitative evaluations, and obtains the best result in the application experiment of underwater object detection.
- underwater image enhancement,
- task-friendly,
- attention mechanism,
- U-Net structure,
- frequency domain perception

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References(38)

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