A brief survey on deep learning based image super-resolution

被引：0
作者：
Zhu X. [1 ]
Li S. [2 ]
Wang L. [3 ]
机构：
[1] Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing
[2] School of Computer and Information Engineering, Beijing Technology and Business University, Beijing
[3] Academy of Broadcasting Science, National Radio and Television Administration, Beijing
来源：
High Technology Letters | 2021年 / 27卷 / 03期
关键词：
Convolutional neural network(CNN); Deep learning; Image super-resolution(SR);
D O I：
10.3772/j.issn.1006-6748.2021.03.008
中图分类号：
学科分类号：
摘要：
Image super-resolution (SR) is an important technique for improving the resolution and quality of images. With the great progress of deep learning, image super-resolution achieves remarkable improvements recently. In this work, a brief survey on recent advances of deep learning based single image super-resolution methods is systematically described. The existing studies of SR techniques are roughly grouped into ten major categories. Besides, some other important issues are also introduced, such as publicly available benchmark datasets and performance evaluation metrics. Finally, this survey is concluded by highlighting four future trends. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.
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收藏
页码：294 / 302
页数：8
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