A Survey on Super Resolution for video Enhancement Using GAN

TL;DR

This survey reviews recent advancements in video super-resolution using GANs, highlighting techniques like recursive learning, novel loss functions, and attention models to enhance video quality across various applications.

Contribution

It provides a comprehensive overview of recent GAN-based methods for video super-resolution, emphasizing new techniques, evaluation criteria, and future research directions.

Findings

GAN-based methods improve video quality metrics like PSNR and SSIM.

Recent techniques include recursive learning and attention mechanisms.

GAN applications extend to surveillance and medical imaging.

Abstract

This compilation of various research paper highlights provides a comprehensive overview of recent developments in super-resolution image and video using deep learning algorithms such as Generative Adversarial Networks. The studies covered in these summaries provide fresh techniques to addressing the issues of improving image and video quality, such as recursive learning for video super-resolution, novel loss functions, frame-rate enhancement, and attention model integration. These approaches are frequently evaluated using criteria such as PSNR, SSIM, and perceptual indices. These advancements, which aim to increase the visual clarity and quality of low-resolution video, have tremendous potential in a variety of sectors ranging from surveillance technology to medical imaging. In addition, this collection delves into the wider field of Generative Adversarial Networks, exploring their principles, training approaches, and applications across a broad range of domains, while also emphasizing the challenges and opportunities for future research in this rapidly advancing and changing field of artificial intelligence.