基于相对总变差统计线的水下图像快速增强方法

陈超洋; 胡盼; 何磊; 易遵辉; 桂卫华

doi:10.16383/j.aas.c240794

基于相对总变差统计线的水下图像快速增强方法

doi: 10.16383/j.aas.c240794 cstr: 32138.14.j.aas.c240794

陈超洋^{1, 2, 3, 4,},
胡盼^{1, 2,},
何磊^3,,
易遵辉^{1, 3,},
桂卫华^5,

1.
湖南科技大学三亚研究院三亚 572025
2.
湖南科技大学计算机科学与工程学院湘潭 411201
3.
湖南科技大学信息与电气工程学院湘潭 411201
4.
湖南科技大学复杂系统智能控制与运维湖南省重点实验室湘潭 411201
5.
中南大学自动化学院长沙 410000

基金项目: 国家自然科学基金(62222306, 62403191, 62403192), 湖南省自然科学基金(2024JJ6223), 海南省自然科学基金(625QN363)资助

详细信息

作者简介:
陈超洋：湖南科技大学信息与电气工程学院教授. 2014年获得华中科技大学博士学位. 主要研究方向为群机器人系统智能感知与协同控制, 复杂网络研究. E-mail: ouzk@163.com

胡盼：湖南科技大学计算机科学与工程学院博士研究生. 2017年获得湖南科技大学硕士学位. 主要研究方向为图像处理和视觉检测. E-mail: hupan0916@163.com

何磊：湖南科技大学信息与电气工程学院讲师. 2023年获得中南大学博士学位. 主要研究方向为视觉检测, 图像处理和深度学习. 本文通信作者. E-mail: helei_xb@hnust.edu.cn

易遵辉：湖南科技大学信息与电气工程学院讲师. 2023年获得中南大学博士学位. 主要研究方向为光学成像, 图像处理, 视觉检测. E-mail: yizunhui@hnust.edu.cn

桂卫华：中南大学自动化学院教授. 1981年获得中南矿冶学院硕士学位. 主要研究方向为复杂工业过程检测、建模与控制. E-mail: gwh@csu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2024-12-13
- 录用日期: 2025-05-14
- 网络出版日期: 2025-06-19

Fast Enhancement Method for Underwater Images Based on Relative Total Variation Statistical Line

CHEN Chao-Yang^{1, 2, 3, 4
,},
HU Pan^{1, 2
,},
HE Lei^3
,,
YI Zun-Hui^{1, 3
,},
GUI Wei-Hua^5
,

1.
Sanya Institute of Hunan University of Science and Technology, Sanya 572025
2.
School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201
3.
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201
4.
Hunan Provincial Key Laboratory of Intelligent Control and Maintenance for Complex Systems, Hunan University of Science and Technology, Xiangtan 411201
5.
School of Automation, Central South University, Changsha 410000

Funds: Supported by National Natural Science Foundation of China (62222306, 62403191, 62403192), Hunan Provincial Natural Science Foundation of China (2024JJ6223) and Hainan Provincial Natural Science Foundation of China (625QN363)

More Information

Author Bio:
CHEN Chao-Yang　Professor at the School of Information and Electrical Engineering, Hunan University of Science and Technology. He received his Ph.D. degree from Huazhong University of Science and Technology in 2014. His research interest covers intelligent sensing and cooperative control of swarm robot systems and complex network research

HU Pan　Ph.D. candidate at the School of Computer Science and Engineering, Hunan University of Science and Technology. He received his master degree from Hunan University of Science and Technology in 2017. His research interest covers image processing and vision-based measurement

HE Lei　Lecturer at the School of Information and Electrical Engineering, Hunan University of Science and Technology. He received his Ph.D. degree from Central South University in 2023. His research interest covers vision-based measurement, image processing and deep learning. Corresponding author of this paper

YI Zun-Hui　Lecturer at the School of Information and Electrical Engineering, Hunan University of Science and Technology. He received his Ph.D. degree from Central South University in 2023. His research interest covers optical imaging, image processing and vision-based measurement

GUI Wei-Hua　Professor at the School of Automation, Central South University. He received his master degree from Central South Institute of Mining and Metallurgy in 1981. His main research interest is measurement, modeling and control of complex industrial process

摘要

摘要: 针对水下采集的图像存在模糊、低对比度和颜色失真等低质量问题, 提出一种基于相对总变差统计线的水下图像快速增强方法. 首先, 采用线性拉伸的方法来校正图像的颜色信息, 消除颜色偏差并恢复图像的自然度. 其次, 基于大气散射模型, 结合图像的纹理信息构建水下图像的相对总变差统计线模型, 利用该模型准确估计图像深度图. 此外, 提出一种基于图像分块细分的水下背景光估计方法, 得到鲁棒的全局背景光估计值. 最后, 在估计的背景光和深度图基础上得到符合人眼感官视觉的水下增强图像. 实验结果表明, 所提方法不仅在主客观图像质量评价上具有明显优势, 而且在计算效率上优于现有的先进方法.
- 水下图像 /
- 相对总变差 /
- 颜色校正 /
- 图像增强 /
- 统计线
Abstract: To address the low-quality issues such as blurring, low contrast and color distortion in underwater images, a fast enhancement method for underwater images based on relative total variation statistical line is proposed. First, linear stretching method is used to correct the color information in the image, eliminate color deviations and restore the image＇s natural appearance. Second, the relative total variation statistical line model of underwater images is constructed based on the atmospheric scattering model, combined with the texture information of the images, and the model is utilized to accurately estimate the image depth map. In addition, an underwater background light estimation method based on image chunk segmentation is proposed to obtain robust global background light estimation value. Finally, an enhanced underwater image conforming to the sensory vision of the human eye is obtained on the basis of the estimated background light and the estimated depth map. Experimental results demonstrate that the proposed method not only exhibits significant advantages in both subjective and objective image quality evaluation but also outperforms existing advanced methods in computational efficiency.
- Underwater images /
- relative total variation /
- color correction /
- image enhancement /
- statistical line

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图 1 示意图((a)水下成像模型; (b)水下光吸收)

Fig. 1 Schematic diagrams ((a) Underwater imaging model; (b) Underwater light absorption)

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图 2 所提方法的算法流程框架

Fig. 2 The algorithm flow framework of the proposed method

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图 3 原始图像与增强图像直方图比较

Fig. 3 Comparison of histograms of raw image and color corrected image

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图 4 局部拟合相对总变差统计线示例

Fig. 4 Example of localized fitted relative total variation statistical line

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图 5 图像块的不同分割方法((a)输入图像; (b)不同图像分割方法; (c)不同图像分割方法下的深度图; (d)比较生成的深度图; (e)平滑的深度图; (f)增强图像)

Fig. 5 Different segmentation methods of image patches ((a) Input image; (b) Different image segmentation methods (c) Depth maps for the different image segmentation methods; (d) Comparison of generated depth maps (e) Smoothed depth map; (f) Enhanced image)

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图 6 对偏绿水下图像不同方法的视觉比较

Fig. 6 Visual comparison of different methods on greenish underwater images

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图 7 对偏蓝水下图像不同方法的视觉比较

Fig. 7 Visual comparison of different methods on bluish underwater images

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图 8 对具有其他颜色退化的水下图像不同方法的视觉比较

Fig. 8 Visual comparison of different methods on underwater images with other color degenerations

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图 9 对水下图像不同方法色彩准确度结果比较

Fig. 9 Color accuracy results comparison of different methods on underwater images

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图 10 对浑浊场景下水下图像不同方法的视觉比较

Fig. 10 Visual comparison of different methods on underwater images in turbid scenes

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图 11 对动态光照场景下水下图像不同方法的视觉比较

Fig. 11 Visual comparison of different methods for underwater images in dynamic lighting scenes

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图 12 去雾图像结果

Fig. 12 Results of dehazed images

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表 1 不同方法的UCIQE值

Table 1 UCIQE values for different methods

序号	原图	MLLE	MILHD	WWPF	Bayesian	CCIA	HLRP	LANet	HUPE	本文方法
1	0.4306	0.6074	0.6569	0.6091	0.5773	0.6203	0.6613	0.5426	0.6007	0.6671
2	0.4230	0.5790	0.6505	0.5882	0.5527	0.5681	0.6419	0.6092	0.5893	0.6518
3	0.4136	0.6063	0.6736	0.6126	0.5962	0.6268	0.6630	0.5528	0.5849	0.6663
4	0.2889	0.6334	0.6528	0.6102	0.6286	0.6394	0.6531	0.3468	0.3266	0.6723
5	0.4258	0.6369	0.7027	0.6414	0.6116	0.6529	0.7037	0.5569	0.5983	0.6835
6	0.3683	0.5707	0.6918	0.5948	0.5873	0.6428	0.6849	0.5113	0.5597	0.6919
平均值	0.3917	0.6056	0.6714	0.6094	0.5923	0.6250	0.6680	0.5199	0.6301	0.6721

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表 2 不同方法的UIQM值

Table 2 UIQM values for different methods

序号	原图	MLLE	MILHD	WWPF	Bayesian	CCIA	HLRP	LANet	HUPE	本文方法
1	0.9615	4.8606	4.6098	3.9214	4.8805	4.4507	3.0289	3.1515	2.9627	4.9770
2	0.7860	4.4556	4.2190	4.5444	4.5228	4.1937	3.1469	4.7041	3.4857	4.7357
3	1.0822	3.7046	4.3577	3.3216	4.0716	4.0807	3.0612	2.9985	3.0857	4.3004
4	1.0064	4.4531	4.6104	3.8922	5.1354	4.4802	3.8992	1.8163	1.3638	4.5014
5	2.3545	4.9188	4.7103	4.0713	4.8579	4.4756	2.5404	4.0396	3.6857	4.6359
6	2.0476	4.9280	4.6299	4.4408	4.9191	4.5424	3.5872	3.8476	3.2337	5.4272
平均值	1.3730	4.5535	4.5228	4.0320	4.7312	4.3706	3.2106	3.4263	2.7806	4.7629

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表 3 算法的运行时间比较

Table 3 Comparison of algorithm runtime

算法	运行时间(s)
MLLE	1.85
MILHD	1.74
WWPF	1.61
Bayesian	1.28
CCIA	0.80
HLRP	1.35
本文方法	0.65

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