Underwater Image Enhancement using Generative Adversarial Networks: A Survey

• URL: http://arxiv.org/abs/2501.06273v1
• Date: Fri, 10 Jan 2025 06:41:19 GMT
• Title: Underwater Image Enhancement using Generative Adversarial Networks: A Survey
• Authors: Kancharagunta Kishan Babu, Ashreen Tabassum, Bommakanti Navaneeth, Tenneti Jahnavi, Yenka Akshaya,
• Abstract summary: Generative Adversarial Networks (GANs) have emerged as a powerful tool for enhancing underwater photos. GANs 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 CNN-based models and state-of-the-art GAN-based methods.
• Score: 1.2582887633807602
• License:
• 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.

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