| 图像超分辨率方法研究进展 |
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| 引用本文： | 谢海平,谢凯利,杨海涛.图像超分辨率方法研究进展[J].计算机工程与应用,2020,56(19):34-41. |
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| 作者姓名： | 谢海平 谢凯利 杨海涛 |
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| 作者单位： | 1.航天工程大学 研究生院，北京 101416
2.航天工程大学 航天信息学院，北京 101416 |
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| 摘 要： | 随着计算机理论与技术的发展，图像超分辨率理论和技术手段不断取得新的进步，发展出插值法、重构法和学习法等一系列方法。报告了图像超分辨率的研究进展，梳理了主要的图像超分辨率方法，阐述了几种较为重要的深度学习超分辨率模型，总结了当前图像超分辨率的发展趋势，对超分辨率的研究提出了展望。
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| 关 键 词： | 超分辨率 图像重构 深度学习 |
Research Progress of Image Super-Resolution Methods |
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| XIE Haiping,XIE Kaili,YANG Haitao.Research Progress of Image Super-Resolution Methods[J].Computer Engineering and Applications,2020,56(19):34-41. |
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| Authors: | XIE Haiping XIE Kaili YANG Haitao |
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| Affiliation: | 1.Graduate School, Space Engineering University, Beijing 101416, China
2.School of Space Information, Space Engineering University, Beijing 101416, China |
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| Abstract: | With the development of computer theory and technology, image super-resolution theory and technology have continuously made new progress, and a series of methods such as interpolation, reconstruction and machine learning have been developed. This paper reports the research progress of image super-resolution. Firstly, it introduces the methods of image super-resolution. Secondly, it summarizes major deep learning super-resolution models and the current development trend of image super-resolution. Finally, the future development and challenges of super-resolution research are proposed. |
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| Keywords: | super-resolution image reconstruction deep learning |
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