Underwater Image Enhancement using Generative Adversarial Networks: A Survey

TL;DR

This survey reviews recent advances in underwater image enhancement using GANs, highlighting methods, datasets, challenges, and future directions in improving underwater visual quality for various applications.

Contribution

It provides a comprehensive analysis of all major approaches, evaluation metrics, and challenges in GAN-based underwater image enhancement.

Findings

GANs effectively improve underwater image quality

Current methods face generalization and computational challenges

Future research should address dataset biases and efficiency

Abstract

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.