RSC-WSRGAN super-resolution reconstruction based on improved generative adversarial network-Reference-Cited by-同舟云学术

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RSC-WSRGAN super-resolution reconstruction based on improved generative adversarial network

Published:2024-07-18 Issue:11 Volume:18 Page:7833-7845
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Tao Peng,Yang Degang

Funder

The work described in this paper was partially supported by National Natural Science Foundation of China

Natural Science Foundation Project of Chongqing

Project Supported by Scientific and Technological Research Program of Chongqing Municipal Education Commission

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11760-024-03432-6.pdf

Reference32 articles.

1. Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 126–135 (2017)

2. Biswas, K., Kumar, S., Banerjee, S., Pandey, A.K.: Smu: smooth activation function for deep networks using smoothing maximum technique. arXiv:2111.04682 (2021)

3. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Proceedings of the 13th European Conference on Computer Vision—ECCV 2014, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part IV 13, Springer, pp. 184–199 (2014)

4. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2015)

5. Gendy, G., Mohammed, H., Sabor, N., He, G.: A deep pyramid attention network for single image super-resolution. In: 2021 9th International Japan-Africa Conference on Electronics, Communications, and Computations (JAC-ECC). IEEE, pp. 14–19 (2021)

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