Image super-resolution reconstruction is an important technique to improve image quality. Thanks to the successful application and rapid development of deep learning in the field of computer vision, significant improvement in single image super-resolution (SISR) reconstruction has been achieved. In response, this study explores SISR reconstruction methods based on deep learning in depth. Relevant background knowledge such as benchmark data sets, performance evaluation indexes, and the loss function used in this field are outlined. Then, the latest algorithms for SISR reconstruction techniques with supervised and unsupervised learning are discussed respectively, and the differences and similarities among different models as well as their advantages and disadvantages are compared. Finally, the existing problems in this field are summarized, and future trends are proposed.