  Image Super-Resolution (SR) is an important class of image processing
techniques to enhance the resolution of images and videos in computer vision.
Recent years have witnessed remarkable progress of image super-resolution using
deep learning techniques. This article aims to provide a comprehensive survey
on recent advances of image super-resolution using deep learning approaches. In
general, we can roughly group the existing studies of SR techniques into three
major categories: supervised SR, unsupervised SR, and domain-specific SR. In
addition, we also cover some other important issues, such as publicly available
benchmark datasets and performance evaluation metrics. Finally, we conclude
this survey by highlighting several future directions and open issues which
should be further addressed by the community in the future.
