Deep learning methods in real-time image super-resolution: a survey-Reference-Cited by-同舟云学术

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Deep learning methods in real-time image super-resolution: a survey

Published:2019-11-16 Issue:6 Volume:17 Page:1885-1909
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Li Xiaofang,Wu Yirui,Zhang Wen,Wang Ruichao,Hou Feng

Funder

National Key R&D Program of China

Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

the open Project of the National Key Lab for Novel Software Technology in NJU

Publisher

Springer Science and Business Media LLC

Subject

Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s11554-019-00925-3.pdf

Reference151 articles.

1. Agustsson, E., Timofte, R., Van Gool, L.: Anchored regression networks applied to age estimation and super resolution. In: Proceedings of International Conference on Computer Vision, pp. 1652–1661 (2017)

2. Ahn, N., Kang, B., Sohn, K.: Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of European Conference on Computer Vision, pp. 256–272 (2018)

3. Akae, N., Makihara, Y., Yagi, Y.: Gait recognition using periodic temporal super resolution for low frame-rate videos. In: Proceedings of International Joint Conference on Biometrics, pp. 1–7 (2011)

4. Alonso-Fernandez, F., Farrugia, R.A., Bigun, J., Fierrez, J., Gonzalez-Sosa, E.: A survey of super-resolution in iris biometrics with evaluation of dictionary-learning. IEEE Access (2018)

5. Aly, H.A., Dubois, E.: Image up-sampling using total-variation regularization with a new observation model. IEEE Trans. Image Process. 14(10), 1647–1659 (2005)

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