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.

通过对公共数据集、评估指标和四类方法的研究以及在基准数据集上的比较，本文全面评估了基于深度学习的单个图像超分辨率（SISR）方法中类别为基于退化建模、基于图像对、基于域转换和基于自学习的 RSISR 方法在重建质量和计算效率方面的表现，并讨论了 RSISR 的挑战和未来研究方向。