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. However, 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 publicly 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.

单幅图像超分辨率 (SISR) 旨在从低分辨率 (LR) 观察中重建高分辨率 (HR) 图像，近几十年来一直是图像处理领域的一个活跃研究课题。特别是基于深度学习的超分辨率（SR）方法引起了广泛关注，并极大地提高了合成数据的重建性能。然而，最近的研究表明，对合成数据的模拟结果通常高估了超分辨率现实世界图像的能力。在这种背景下，越来越多的研究人员致力于开发逼真图像的 SR 方法。本文旨在对现实世界的单幅图像超分辨率（RSISR）进行全面回顾。更具体地说，本次审查涵盖了 RSISR 的关键公开可用数据集和评估指标，以及四大类 RSISR 方法，即基于退化建模的 RSISR、基于图像对的 RSISR、基于域翻译的 RSISR 和基于自学习的 RSISR。还对基准数据集上的代表性 RSISR 方法在重建质量和计算效率方面进行了比较。此外，我们讨论了 RSISR 的挑战和有前途的研究课题。