Image super-resolution: A comprehensive review, recent trends, challenges and applications

Abstract

Super resolution (SR) is an eminent system in the field of computer vison and image processing to improve the visual perception of the poor-quality images. The key objective of image super resolution is to address the limitations of imaging systems mainly due to hardware problems and requirements for clinical processing of medical imaging using post-processing operations. Numerous super resolution strategies have been put-forward in the computer vision community to improve and achieve high-resolution images over the years. In the past few years, there has been a significant advancement in image super-resolution algorithms. This paper aims to provide the detailed survey on recent advancements in image super-resolution in terms of traditional, deep learning and the latest transformer-based algorithms. The in-depth taxonomy of broadly classified super-resolution techniques within these categories has been broadly discussed. An extensive survey has been carried out on deep learning techniques in terms of parameters, architecture, network complexity, depth, learning rate, framework, optimization, and loss function. Furthermore, we also address some of the significant parameters such as problem definition, evaluation metrics, publicly benchmarks datasets, loss functions and applications. In addition, we have performed an experimental analysis and comparison of various benchmark algorithms on publicly available datasets both qualitively and quantitively. Lastly, we conclude our survey by emphasizing some of the prospective future directions and open issues that the community need to address in the future.