A Comprehensive Review and Comparison of Image Super-resolution Techniques

Abstract

Image super-resolution (SR) is a pivotal task in computer vision and image processing, aiming to enhance the resolution and quality of low-resolution images. This review article provides an in-depth analysis and comparison of various image super-resolution techniques, including traditional methods and deep learning-based approaches. We discuss the underlying principles, algorithms, advantages, and limitations of each technique, along with their applications across diverse domains. Additionally, we highlight recent advancements, challenges, and future research directions in the field of image super-resolution.