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Deep Learning for Image Super-resolution: A Survey

2019-02-16 · Zhihao Wang, Jian Chen, Steven C. H. Hoi

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.

📄 PDF Abstract BibTeX arXiv:1902.06068

Code (5)

ptkin/Awesome-Super-Resolution 공식 구현 pytorch
Idelcads/IMKI_Technical_test pytorch
Idelcads/Super_Resolution_overview pytorch
darren622672/IQA
impredicative/irc-url-title-bot tf

Tasks

Deep LearningImage Super-ResolutionSuper-ResolutionSurvey

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