Deep multi‐level up‐projection network for single image super‐resolution-Reference-Cited by-同舟云学术

Deep multi‐level up‐projection network for single image super‐resolution

Published:2020-12-13 Issue:2 Volume:15 Page:325-336
ISSN:1751-9659
Container-title:IET Image Processing
language:en
Short-container-title:IET image process

Author:

Shen Yan¹,Zhang Liao¹,Chen Yun²,Xie Yi³,Wang Zhongli¹,Shao Xiaotao¹

Affiliation:

1. School of Electronic and Information Engineering Beijing Jiaotong University Beijing 100044 P. R. China

2. Shanghai Institute of Spacelight Control Technology Shanghai 201109 P. R. China

3. Beijing Xinghang Mechanical‐Electrical Equipment Co., LTD Beijing 100074 P. R. China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/ipr2.12014

Reference30 articles.

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2. Kim J. et al.:Accurate image super‐resolution using very deep convolutional networks. In:Proceedings of IEEE Conference on Computer Vision and Pattern Recognition pp.1646‐1654.IEEE Piscataway NJ(2016)

3. Zhang K. et al.:Learning deep CNN denoiser prior for image restoration. In:Proceedings of IEEE Conference Computer Vision and Pattern Recognition pp.3929‐3938.IEEE Piscataway NJ(2017)

4. Bee L. et al.:Enhanced deep residual networks for single image super‐resolution. In:The IEEE Conf. on Computer Vision and Pattern Recognition Workshops IEEE Piscataway NJ(2017)

5. Ying T. et al.:Memnet: a persistent memory network for image restoration. In:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE Piscataway NJ(2017)

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