Recent Advances in Deep Learning for Single Image Super-Resolution

摘要

Image super-resolution is an important research field in image analysis. The techniques of image super-resolution has been widely used in many computer vision applications. In recent years, the success of deep learning methods in image super-resolution have attracted more and more researchers. This paper gives a brief review of recent deep learning based methods for single image super-resolution (SISR), in terms of network type, network structure, and training methods. The advantages and disadvantages of these methods are analyzed as well.