Abstract: Single image super-resolution (SISR) refers to the recovery of a high-resolution image from a single low-resolution image. With deep learning used in the field of image super-resolution, deep networks can independently learn the mapping relationship between low-resolution and high-resolution training images, showing better reconstruction performance than the traditional methods. Therefore, deep learning has become dominant in super-resolution. This paper focuses on the exploration of the existing deep network model of super-resolution in terms of reconstruction mode, network structure, and loss function. By comparing the similarities and differences between different models, the advan- tages and disadvantages of different model building methods and the applicable application scenarios are analyzed. Meanwhile, the reconstruction results of different network models on the benchmark test datasets are compared and the potential directions are concluded.

Key words: deep learning, super-resolution reconstruction, neural network, information fusion