AquaGAN: Restoration of Underwater Images

Reddy, Badduri Sai Sudheer; Tabib, Ramesh Ashok; Patil, Ujwala; Mudenagudi, Uma

doi:10.1109/cvprw56347.2022.00044

Cited by 15 publications

(1 citation statement)

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“…GANs have achieved amazing performance even in extremely complex application (e.g., underwater) [165]- [167]. A representative work, AquaGAN [168], proposes a weighted combination of content and style loss for the first time, and generates clean underwater images. It is worth noting that the attenuation coefficient in AquaGAN is very sensitive, and the recovery results corresponding to different values vary significantly.…”

Section: B Gan-based Methodsmentioning

confidence: 99%

A Comprehensive Review of Deep Learning-Based Real-World Image Restoration

et al. 2023

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Real-world imagery does not always exhibit good visibility and clean content, but often suffers from various kinds of degradations (e.g., noise, blur, rain drops, fog, color distortion, etc.), which severely affect vision-driven tasks (e.g., image classification, target recognition, and tracking, etc.). Thus, restoring the true scene from such degraded images is of significance. In recent years, a large body of deep learning-based image processing works has been exploited due to the advances in deep neural networks. This paper aims to make a comprehensive review of real-world image restoration algorithms and beyond. More specifically, this review provides overviews of critical benchmark datasets, image quality assessment methods, and four major categories of deep learning-based image restoration methods, i.e., based on convolutional neural network (CNN), generative adversarial network (GAN), Transformer, and multilayer perceptron (MLP). The paper highlights the latest developments and advances in each category of network architecture to provide an up-to-date overview. Moreover, the representative state-of-the-art image restoration methods are compared visually and numerically. Finally, for real-world image restoration, the current situations are objectively assessed, challenges are discussed, and future directions and trends are presented.

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Section: B Gan-based Methodsmentioning

confidence: 99%