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.

单图像超分辨率（SISR）是图像处理中的一项重要任务，旨在提高成像系统的分辨率。最近，SISR 在深度学习（DL）的帮助下取得了巨大的飞跃并取得了可喜的成果。在本次调查中，我们概述了基于深度学习的 SISR 方法，并根据其设计目标对它们进行了分组。具体来说，我们首先介绍了问题的定义、研究背景以及SISR的意义。其次，我们介绍了一些相关工作，包括基准数据集、上采样方法、优化目标和图像质量评估方法。第三，我们对 SISR 进行了详细的研究，并给出了它的一些特定领域的应用。第四，我们展示了一些经典 SISR 方法的重建结果，以直观地了解它们的性能。最后，我们讨论了SISR中仍然存在的一些问题，并总结了一些新的趋势和未来的方向。这是对 SISR 的详尽调查，可以帮助研究人员更好地了解 SISR 并激发该领域更令人兴奋的研究。 https://github.com/CV-Jun ChengLi/SISR-Survey 提供了 SISR 的调查项目。