Image super-resolution (SR) reconstruction refers to the generation of a corresponding high Resolution image from a low Resolution image. SR has important application value in monitoring, remote sensing, digital HD, video coding communication and other fields. In this paper, we first review the history of single image SR and summarize the non-learning based SR methods. Among them, interpolation and reconstruction based methods are introduced, and then learning-based methods are introduced, in which SR based on deep learning is analyzed such as SRCNN, ESPCN and SRGAN are summarized in detail, and compared with recursive structure, dense structure and attention mechanism network structure. Then the function of loss function and upsampling method in image SR is analyzed, common data sets and image evaluation indexes are introduced, and visualization results of image SR are displayed. Finally, the progress and deficiency of single image super-resolution technology are summarized.