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About this paper
Kancharagunta Kishan Babu, Ashreen Tabassum, Bommakanti Navaneeth, Tenneti Jahnavi, Yenka Akshaya
A survey systematically reviews Generative Adversarial Networks (GANs) for underwater image enhancement, detailing their evolution, architectures, datasets, and evaluation metrics. The paper consolidates the state-of-the-art while identifying key challenges and future research directions for robust underwater visual tasks.
Key insights
- GANs offer superior realism and fidelity in restoring colors, details, and textures in degraded underwater images compared to traditional and earlier CNN methods due to their adversarial training.
- Different GAN architectures (e.g., CycleGAN for unpaired data, FUnIE-GAN for real-time) address specific challenges, showcasing diverse applications and trade-offs in performance and efficiency.
- The field commonly utilizes datasets like EUVP and UIEBD, and evaluates performance with metrics such as PSNR, SSIM, and UCIQE, often employing adversarial, L1/L2, and perceptual loss functions.
Summary by alphaXiv.
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