视觉任务友好的水下图像增强方法

程淼; 魏延辉; 孙文斌; 侯童童

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

视觉任务友好的水下图像增强方法

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

程淼^1,,
魏延辉^{1, 2, 3, ,},
孙文斌^{2, 3,},
侯童童^{2, 3,}

1.
哈尔滨工程大学三亚南海创新发展基地, 海南三亚, 572024
2.
哈尔滨工程大学智能科学与工程学院, 黑龙江哈尔滨, 150001
3.
黑龙江省海洋智能装备与仪器重点实验室, 黑龙江哈尔滨, 150001

基金项目: 海南省科技计划三亚崖州湾科技城科技创新联合项目(2021CXLH0001); 海南省外国专家项目(海南省国际科技合作人才与交流项目)(G20230607011E); 三亚市科技创新专项拟立项项目(2022KJCX42).

详细信息

作者简介:
程淼：程　淼(2002-), 女, 在读硕士, 主要研究方向为水下图像处理和目标检测

通讯作者:
魏延辉(1978-), 男, 教授, 工学博士, 主要研究方向为可重构机器人运动控制, 水下机器人导航定位控制等

中图分类号: TP391.41; TN911.73
计量
- 文章访问数: 8
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- PDF下载量: 0
- 被引次数: 0
出版历程
- 收稿日期: 2026-01-20
- 修回日期: 2026-02-15
- 录用日期: 2026-03-09
- 网络出版日期: 2026-03-30

Method of Vision-Task-Friendly Underwater Image Enhancement

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

摘要

摘要: 水下图像因遭受严重的色彩和结构失真, 影响各种水下视觉任务的表现。现有的水下图像增强方法侧重于改善视觉外观, 忽略优化下游视觉任务的必要性。为此, 文中提出了一种视觉任务友好的水下图像增强方法(VTF-Net)。首先设计了全新的空域频域融合增强模块(SFF), 该模块能大幅提高模型对纹理细节的感知度和图像的保真度; 其次为了实现编码器和解码器之间信息的高效传递, 文中引入多尺度交叉注意力模块(MSCA)和瓶颈注意力模块(BNA), 在保证高效特征提取的基础上增加对全局梯度的感知, 有效改善图像的色偏和模糊问题。最后针对视觉任务友好的理念, 提出一种检测损失函数, 通过引入水下目标检测结果引导模型优化方向。实验结果表明: 文中所提方法在定性和定量实验中均取得了更好的结果, 同时在水下目标检测应用实验中取得了最优的结果。
- 水下图像增强 /
- 任务友好 /
- 注意力机制 /
- U-Net结构 /
- 频域感知
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

HTML全文

图 1 VTF-Net整体结构图

Figure 1. Overall structure diagram of the VTF-Net

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图 2 SFF结构图

Figure 2. Structure diagram of the SFF

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图 3 MSCA结构图

Figure 3. Detailed structure diagram of the MSCA

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图 4 BNA结构图

Figure 4. Detailed structure diagram of the BNA

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图 5 不同方法在不同退化风格图像上的定性对比结果(I: 偏绿; II: 偏蓝; III: 非均匀光照; IV: 低光; V: 连续)

Figure 5. Qualitative comparison results of different methods on images with different degraded styles (I: slightly green; II: slightly blue; III: non-uniform lighting; IV: low light; V: continuous)

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图 6 不同UIE方法在UFO120上的定量(PSNR)对比结果

Figure 6. Quantitative comparison results (PSNR) of different UIE methods on the UFO120

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图 7 消融实验结果

Figure 7. The result of ablation experiment

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表 1 不同方法在UFO120和EUVP515测试集上的定量对比分析

Table 1. Quantitative comparative analysis of different methods on the UFO120 and EUVP515 test sets

	UFO120				EUVP515				参数量×10⁶	单帧耗时/s
	PSNR↑	SSIM↑	UIQM↑	UCIQE↑	PSNR↑	SSIM↑	UIQM↑	UCIQE↑	参数量×10⁶	单帧耗时/s
Fusion	17.58	0.81	5.10	0.45	17.63	0.81	4.32	0.43	—	0.240
WFAC	15.37	0.68	4.01	0.43	15.65	0.69	3.93	0.43	—	0.359
WWPE	15.64	0.69	4.37	0.45	15.84	0.69	4.15	0.45	—	0.388
WaterNet	19.70	0.86	4.57	0.43	19.68	0.86	4.38	0.44	1.09	0.034
HUPE	18.22	0.83	4.70	0.44	18.07	0.82	4.38	0.44	2.05	1.360
PUIE	19.00	0.84	4.28	0.41	19.10	0.84	4.01	0.40	1.01	0.545
FUnIE-GAN	24.83	0.85	4.64	0.43	23.52	0.85	4.32	0.43	7.02	0.094
VTF-Net	25.32	0.87	4.77	0.45	24.77	0.86	4.33	0.43	6.50	0.164

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表 2 各个模块的消融实验结果

Table 2. The ablation experiment results of each modules

	PSNR↑	SSIM↑	UIQM↑	UCIQE↑
VTF-Net	22.819	0.834	4.204	0.396
无SFF	21.540	0.821	4.117	0.367
无MSCA	21.847	0.829	4.177	0.392
无BNA	22.631	0.832	4.089	0.394

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表 3 不同算法的检测对比分析

Table 3. Comparison and analysis of detection results using different algorithms

	Fusion	WFAC	WWPE	WaterNet	HUPE	PUIE	FUnIE- GAN	VTF- Net
目标数量	57	67	51	57	42	59	47	83
mAP	0.898	0.890	0.891	0.878	0.897	0.898	0.884	0.901

下载: 导出CSV

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