Deep Learning for Image Super-resolution: A Survey
It provides a comprehensive overview for researchers in computer vision, but is incremental as it summarizes existing work without new results.
This survey paper compiles recent advances in image super-resolution using deep learning, categorizing techniques into supervised, unsupervised, and domain-specific approaches, and discusses datasets, metrics, and future directions.
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.