# Underwater Image Enhancement using Generative Adversarial Networks: A Survey

arXiv: https://arxiv.org/abs/2501.06273 · HTML: https://mlsanity.com/paper/2501.06273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 73 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [U-shape Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2111.11843/) (2021)
- [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018)
- [Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer](https://mlsanity.com/paper/1710.07084/) (2017)
- [UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space](https://mlsanity.com/paper/2103.07138/) (2021)
- [UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer](https://mlsanity.com/paper/2310.20210/) (2023)
- [MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility](https://mlsanity.com/paper/2312.15633/) (2023)
- [CPDM: Content-Preserving Diffusion Model for Underwater Image Enhancement](https://mlsanity.com/paper/2401.15649/) (2024)

## Built on by (most cited first, 1 of 1)

- [Applications and Effect Evaluation of Generative Adversarial Networks in Semi-Supervised Learning](https://mlsanity.com/paper/2505.19522/) (2025)

## Abstract (arXiv)

In recent years, there has been a surge of research focused on underwater image enhancement using Generative Adversarial Networks (GANs), driven by the need to overcome the challenges posed by underwater environments. Issues such as light attenuation, scattering, and color distortion severely degrade the quality of underwater images, limiting their use in critical applications. Generative Adversarial Networks (GANs) have emerged as a powerful tool for enhancing underwater photos due to their ability to learn complex transformations and generate realistic outputs. These advancements have been applied to real-world applications, including marine biology and ecosystem monitoring, coral reef health assessment, underwater archaeology, and autonomous underwater vehicle (AUV) navigation. This paper explores all major approaches to underwater image enhancement, from physical and physics-free models to Convolutional Neural Network (CNN)-based models and state-of-the-art GAN-based methods. It provides a comprehensive analysis of these methods, evaluation metrics, datasets, and loss functions, offering a holistic view of the field. Furthermore, the paper delves into the limitations and challenges faced by current methods, such as generalization issues, high computational demands, and dataset biases, while suggesting potential directions for future research.