A Systematic Survey of Deep Learning-based Single-Image Super-Resolution

TL;DR

This comprehensive survey reviews deep learning-based single-image super-resolution methods, discussing their design, datasets, optimization, applications, and future challenges to guide researchers in advancing the field.

Contribution

It provides an exhaustive overview of DL-based SISR techniques, categorizing methods, analyzing performance, and highlighting future research directions.

Findings

Deep learning has significantly advanced SISR performance.

Benchmark datasets and evaluation methods are crucial for progress.

Identified challenges and future trends in SISR research.

Abstract

Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their design targets. Specifically, we first introduce the problem definition, research background, and the significance of SISR. Secondly, we introduce some related works, including benchmark datasets, upsampling methods, optimization objectives, and image quality assessment methods. Thirdly, we provide a detailed investigation of SISR and give some domain-specific applications of it. Fourthly, we present the reconstruction results of some classic SISR methods to intuitively know their performance. Finally, we discuss some issues that still exist in SISR and summarize some new trends and future directions. This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field. An investigation project for SISR is provided at https://github.com/CV-JunchengLi/SISR-Survey.