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Research Area: Machine Learning
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.
Keywords:
Author(s) Name: Zhihao Wang; Jian Chen; Steven C. H. Hoi
Journal name: IEEE Transactions on Pattern Analysis and Machine Intelligence
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TPAMI.2020.2982166
Volume Information: Volume: 43, Issue: 10, 01 October 2021
