Multi-distortion type underwater image enhancement based on improved CycleGAN

doi:10.3785/j.issn.1008-973X.2025.06.006

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (6): 1148-1158 DOI: 10.3785/j.issn.1008-973X.2025.06.006

Multi-distortion type underwater image enhancement based on improved CycleGAN

Zhenming LV1(

),Shaojiang DONG1,*(

),Zongyou XIA2,Xiaoyan MOU3,Mingquan WANG4

1. School of Mechantronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2. College of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China
3. School of Mechanical Engineering, Chongqing Vocational and Technical College of Industry, Chongqing 402260, China
4. Chongqing Survey Institute, Chongqing 400020, China

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Abstract

A multi-distortion type underwater image enhancement algorithm based on improved CycleGAN was proposed, aiming at the difficulties of underwater image blurring, low contrast and image distortion recognition caused by various factors such as scattering, absorption and color deviation. Firstly, in order to improve the image enhancement effect, Auto-Encoder+Skip-connection network structure was used in the generator of CycleGAN, and global color correction structure was added for global enhancement in terms of pixel as well as color, so as to better capture the color information in underwater images. Secondly, a multidimensional perceptual discriminator was designed to learn the global and local features of the image. This discriminator payed more attention to the local details of the image, effectively targeted scattering and color noise, perceived the image from a multidimensional space, and had a stronger ability to extract the features, thereby enhancing the accuracy of image discrimination. Finally, the experimental results on EUVP, UIEB and U45 datasets showed that the proposed method achieved better results, compared with other algorithms. In processing multi-distortion types of underwater images, the algorithm’s SSIM indicator was higher than that of the second place by an average of 1.57%, the PSNR indicator was higher by 1.836%, the UIQM indicator was higher by 1.324%, and the UCIQE indicator was higher by 1.086%. The proposed method performed well in processing color and noise details.

Key words： image enhancement CycleGAN global color correction structure multidimensional perceptual discriminator multiple distortion

Received: 08 April 2024 Published: 30 May 2025

CLC:

TP 391.41

Fund: 重庆市教委科学技术研究资助项目（KJQN202203207)；重庆市科技创新领军人才支持计划资助项目(CSTCCCXLJRC201920)；重庆市高校创新研究群体(CXQT20019).

Corresponding Authors: Shaojiang DONG E-mail: 13946501711@163.com;dongshaojiang100@163.com

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	Zhenming LV
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Cite this article:

Zhenming LV,Shaojiang DONG,Zongyou XIA,Xiaoyan MOU,Mingquan WANG. Multi-distortion type underwater image enhancement based on improved CycleGAN. Journal of ZheJiang University (Engineering Science), 2025, 59(6): 1148-1158.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.06.006 OR https://www.zjujournals.com/eng/Y2025/V59/I6/1148

基于改进CycleGAN的多失真类型水下图像增强

针对由散射、吸收和色偏等多种因素导致的水下图像模糊、对比度低、图像失真辨识困难的问题，提出基于改进CycleGAN的多失真类型水下图像增强算法. 为了提高图像增强效果，在CycleGAN的生成器中采用Auto-Encoder+Skip-connection的网络结构，添加全局色彩校正结构，从像素方面以及颜色方面进行全局增强，从而更好地捕捉水下图像中的色彩信息. 设计多维感知判别器来学习图像的全局特征和局部特征，此设计更加注重图像局部细节部分，有效针对散斑和色彩噪声，从多维空间上感知图像，提取特征能力更强，从而能提高图像判别的精度. 在EUVP、UIEB和U45数据集上的实验结果表明，所提出的方法取得了较好的结果，相较其他算法，在处理多种水下失真类型的图像方面，该算法的SSIM指标平均比第2名高出1.57%、PSNR指标高出1.836%、UIQM指标高出1.324%、UCIQE指标高出1.086%，在处理颜色和噪声细节方面表现出色.

关键词： 图像增强, CycleGAN, 全局色彩校正结构, 多维感知判别器, 多种失真

Fig.1 Overall network architecture for multi-distortion type underwater image

Fig.2 Underwater image enhancement network generator model structure

Fig.3 Squeeze-and-attention module

Fig.4 Self-attention module in generator neck

Fig.5 Discriminator model structure of underwater image enhancement network

Fig.6 Visual comparison of yellow, fogged and green underwater image enhancement methods (local difference amplification representation)

Fig.7 Visual comparison of blue and color underwater image enhancement methods (local difference amplification representation)

Fig.8 Visual comparison of various underwater image enhancement methods on U45 dataset (local difference amplification representation)

Tab.1 Quantitative evaluation results of several algorithms for yellow distorted images on EUVP and UIEB

Tab.2 Quantitative evaluation results of several algorithms for foggy distorted images on EUVP and UIEB

Tab.3 Quantitative evaluation results of blue distorted images on EUVP and UIEB using several algorithms

Tab.4 Quantitative evaluation results of color distorted images on EUVP and UIEB using several algorithms

Tab.5 Quantitative evaluation results of green distorted images on EUVP and UIEB using several algorithms

Tab.6 Quantitative evaluation results of several algorithms in U45

Fig.9 Visual comparison of ablation experiments (local difference amplification representation)

Tab.7 Quantitative assessment of ablation experiments

Tab.8 Quantitative evaluation of loss function ablation experiment


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