Real-World Single Image Super-Resolution: A Brief Review

TL;DR

This paper reviews recent advances in real-world single image super-resolution, highlighting datasets, evaluation metrics, four main methodological categories, and discussing challenges and future research directions.

Contribution

It provides a comprehensive overview of RSISR methods, datasets, metrics, and comparative analysis, addressing the gap between synthetic and real-world image super-resolution.

Findings

Comparison of RSISR methods on benchmark datasets

Analysis of reconstruction quality and efficiency

Discussion of challenges and future research topics

Abstract

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) observation, has been an active research topic in the area of image processing in recent decades. Particularly, deep learning-based super-resolution (SR) approaches have drawn much attention and have greatly improved the reconstruction performance on synthetic data. Recent studies show that simulation results on synthetic data usually overestimate the capacity to super-resolve real-world images. In this context, more and more researchers devote themselves to develop SR approaches for realistic images. This article aims to make a comprehensive review on real-world single image super-resolution (RSISR). More specifically, this review covers the critical publically available datasets and assessment metrics for RSISR, and four major categories of RSISR methods, namely the degradation modeling-based RSISR, image pairs-based RSISR, domain translation-based RSISR, and self-learning-based RSISR. Comparisons are also made among representative RSISR methods on benchmark datasets, in terms of both reconstruction quality and computational efficiency. Besides, we discuss challenges and promising research topics on RSISR.