Benchmark (surveying)

Cartography

Image processing

Contextual image classification

Computer science

Resolution (logic)

Image resolution

Pattern recognition (psychology)

Mathematics

Geography

Image (mathematics)

Domain (mathematical analysis)

Mathematical analysis

Artificial intelligence

Deep learning

Machine learning

IEEE Transactions on Pattern Analysis and Machine Intelligence

Image Super-Resolution (SR) is an important class of image processing techniqueso 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.

Deep Learning for Image Super-Resolution: A Survey