| Zhu Xiaobin (祝晓斌)*,Li Shanshan**,Wang Lei***.[J].高技术通讯(英文),2021,27(3):294~302 |
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| A brief survey on deep learning based image super-resolution |
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| DOI： |
| 中文关键词: |
| 英文关键词: image super-resolution(SR), deep learning, convolutional neural network(CNN) |
| 基金项目: |
| Author Name | Affiliation | | Zhu Xiaobin (祝晓斌)* | (*Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100048, P.R.China)
(**School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, P.R.China)
(***Academy of Broadcasting Science, National Radio and Television Administration, Beijing 100866, P.R.China) | | Li Shanshan** | (*Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100048, P.R.China)
(**School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, P.R.China)
(***Academy of Broadcasting Science, National Radio and Television Administration, Beijing 100866, P.R.China) | | Wang Lei*** | (*Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100048, P.R.China)
(**School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, P.R.China)
(***Academy of Broadcasting Science, National Radio and Television Administration, Beijing 100866, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| 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. |
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