A Survey on Super Resolution for video Enhancement Using GAN

TL;DR

This survey addresses super-resolution for images and videos using Generative Adversarial Networks, focusing on perceptual quality improvements over traditional pixel-based metrics. It synthesizes key GAN-based SR methods (SRGAN, ESRGAN, MR-SRGAN, MDCN) and video SR techniques (frame-rate upsampling, temporal alignment) and analyzes their loss functions, architectural innovations, and training challenges. The paper highlights how downstream metrics such as PSNR, SSIM, and perceptual indices are used to evaluate realism and detail, while acknowledging gaps in real-world degradation modeling and data requirements. It also discusses potential applications across surveillance, medical imaging, and remote sensing, and points to future directions in robust evaluation, unsupervised SR, and efficient architectures.

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.