  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.
