Deep Learning for Image Super-resolution: A Survey

TL;DR

This survey reviews recent deep learning methods for image super-resolution, categorizing them into supervised, unsupervised, and domain-specific approaches, and discusses datasets, metrics, and future challenges.

Contribution

It provides a comprehensive overview of recent deep learning-based super-resolution techniques, highlighting their categories, datasets, evaluation metrics, and future research directions.

Findings

Deep learning has significantly advanced image super-resolution.

Supervised, unsupervised, and domain-specific methods are key categories.

Open issues and future directions are identified for further research.

Abstract

Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future directions and open issues which should be further addressed by the community in the future.