Analysis of Single Image Super-Resolution Techniques: An Evolutionary Study

Abstract

Single image super-resolution (SR) is a technique that reconstructs a high-resolution (HR) image from a single low-resolution (LR) input image. The main objective of super-resolution algorithms is to achieve a high-resolution image that is consistent with the input low-resolution image but has enhanced spectral properties. In this review, several research papers and their corresponding algorithms have been reviewed and are classified based on their methodology. The principal objective of this review is to understand the evolution of SISR techniques from basic interpolation techniques to sophisticated convolutional neural networks. This article also presents design considerations for future advancements.