Deep Learning for Image Super-Resolution: A Survey
Deep Learning for Image Super-Resolution: A Survey
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DOI：
10.1109/tpami.2020.2982166
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发表时间：
2021-10-01
影响因子：
23.6
通讯作者：
Hoi, Steven C. H.
中科院分区：
文献类型：
--
作者：
Wang, Zhihao;Chen, Jian;Hoi, Steven C. H.
图像超分辨率（Image Super-Resolution，SR）是计算机视觉中提高图像和视频分辨率的一类重要的图像处理技术。近年来，基于深度学习的图像超分辨率研究取得了显著的进展。本文旨在全面综述使用深度学习方法实现图像超分辨率的最新进展。一般而言，我们可以粗略地将现有的SR技术研究分为三大类：有监督SR、无监督SR和特定领域SR。此外，我们还涵盖了一些其他重要问题，如公开的基准数据集和性能评估指标。最后，我们总结了几个未来的发展方向和有待社会进一步解决的问题。
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.