Paper Contents
Abstract
Super resolution (SR) algorithms have emerged as a pivotal area of research in computer vision and image processing, aiming to enhance the resolution and quality of digital images. This paper presents a comprehensive overview of various SR algorithms, exploring both traditional methods and contemporary deep learning approaches. The discussion encompasses interpolation-based techniques, edge-based methods, and frequency domain approaches in the traditional realm, while delving into Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and their variants within the domain of deep learning.
Copyright
Copyright © 2024 Spoorthi H S . This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS40100005149
ISSN:
2321-9653
Publisher:
ijprems