FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement

NIU Yu-zhen; ZHANG Ling-xin; LAN Jie; XU Rui; KE Xiao

doi:10.12263/DZXB.20240265

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FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement

PAPERS | 更新时间：2025-12-08

- FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement
- ACTA ELECTRONICA SINICA Vol. 53, Issue 2, Pages: 527-544(2025)
- 作者机构：
  
  福州大学计算机与大数据学院，福建福州 350108
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(U21A20472;61972097);National Key Research and Development Plan of China(2021YFB3600503);Natural Science Foundation of Fujian Province(2021HZ022007);Natural Science Foundation of Fujian Province(2023J01067;2020J01494);Industry-Academy Cooperation Project of Fujian Province (China)(2021H6022)
- DOI：10.12263/DZXB.20240265
  CLC： TP391;
- Received：25 March 2024，
  
  Revised：2024-11-09，
  
  Published：25 February 2025
- 稿件说明：
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牛玉贞, 张凌昕, 兰杰, 等. 基于分频式生成对抗网络的非成对水下图像增强[J]. 电子学报, 2025, 53(02): 527-544.

NIU Yu-zhen, ZHANG Ling-xin, LAN Jie, et al. FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement[J]. Acta Electronica Sinica, 2025, 53(02): 527-544.
牛玉贞, 张凌昕, 兰杰, 等. 基于分频式生成对抗网络的非成对水下图像增强[J]. 电子学报, 2025, 53(02): 527-544. DOI：10.12263/DZXB.20240265

NIU Yu-zhen, ZHANG Ling-xin, LAN Jie, et al. FD-GAN: Frequency-Decomposed Generative Adversarial Network for Unpaired Underwater Image Enhancement[J]. Acta Electronica Sinica, 2025, 53(02): 527-544. DOI：10.12263/DZXB.20240265

摘要

增强水下图像质量对水下作业领域的发展具有重要意义.现有的水下图像增强方法通常基于成对的水下图像和参考图像进行训练，然而实际获取与水下图像对应的参考图像比较困难，相比之下获得非成对高质量水下图像或者陆上图像较为容易.此外，现有的水下图像增强方法很难同时针对各种失真类型进行图像增强.为了避免对成对训练数据的依赖和进一步降低获得训练数据的难度，并应对多样的水下图像失真类型，本文提出了一种基于分频式生成对抗网络（Frequency-Decomposed Generative Adversarial Network，FD-GAN）的非成对水下图像增强方法，并在此基础上设计了高低频双分支生成器用于重建高质量水下增强图像.具体来说，本文引入特征级别的小波变换将特征分为低频和高频部分，并基于循环一致性生成对抗网络对低频和高频部分区分处理.其中，低频分支采用结合低频注意力机制的编码-解码器结构实现对图像颜色和亮度的增强，高频分支则采用并行的高频注意力机制对各高频分量进行增强，从而实现对图像细节的恢复.在多个标准水下图像数据集上的实验结果表明，本文提出的方法在使用非成对的高质量水下图像和引入部分陆上图像的情况下，均能有效生成高质量的水下增强图像，且有效性和泛化性均优于当前主流的水下图像增强方法.

Abstract

Enhancing the quality of underwater images is crucial for advancements in the fields of underwater exploration and underwater rescue. Existing underwater image enhancement methods typically rely on paired underwater images and reference images for training. However

obtaining corresponding reference images for underwater images is challenging in practice. In contrast

acquiring high-quality unpaired underwater images or images captured on land are relatively more straightforward. Furthermore

existing techniques for underwater image enhancement often struggle to address a variety of distortion types simultaneously. To avoid the reliance on paired training data

reduce the difficulty of acquiring training data

and effectively handle diverse types of underwater image distortions

in this paper

we propose a novel unpaired underwater image enhancement method based on the frequency-decomposed generative adversarial network (FD-GAN). We design a dual-branch generator based on high and low frequencies to reconstruct high-quality underwater images. Specifically

feature-level wavelet transform is introduced to separate the features into low-frequency and high-frequency parts. Then the separated features are processed by a cycle-consistent generative adversarial network

so as to simultaneously enhance the color and luminance in the low-frequency component and details in the high-frequency part. More specific

the low-frequency branch employs an encoder-decoder structure with a low-frequency attention mechanism to enhance the color and brightness of the image. The high-frequency branch utilizes parallel high-frequency attention mechanisms to enhance various high-frequency components

thereby achieving the restoration of image details. Experimental results on multiple datasets show that the proposed method trained with unpaired high-quality underwater images or unpaired high-quality underwater images and on-land images

can effectively generate high-quality underwater enhanced images and the proposed method is superior to the state-of-the-art underwater image enhancement methods in terms of effectiveness and generalization.

关键词

Keywords

references

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