A New Era of Artificial Intelligence: Enhancing Video Quality with Cutting-Edge Techniques

Abstract

High-resolution images and videos are essential across various industries, containing critical details and information. However, achieving this level of quality often requires advanced sensors and optical systems, which are expensive and limited in their capabilities. A cost-effective alternative is to enhance the resolution of low-quality images through computational techniques. Among these, the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) has emerged as a breakthrough, producing highly realistic textures and superior image quality. This paper explores ESRGAN's advancements in neural network architecture, loss functions, and training methods, highlighting how it addresses common challenges like unrealistic textures and artifacts in super-resolution tasks.