CGA-Net: channel-wise gated attention network for improved super-resolution in remote sensing imagery-Reference-Cited by-同舟云学术

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CGA-Net: channel-wise gated attention network for improved super-resolution in remote sensing imagery

Published:2023-10-24 Issue:6 Volume:34 Page:
ISSN:0932-8092
Container-title:Machine Vision and Applications
language:en
Short-container-title:Machine Vision and Applications

Author:

Khan Bostan,Mumtaz Adeel,Zafar Zuhair,Sedkey Mohamed,Benkhelifa Elhadj,Fraz Muhammad Moazam

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s00138-023-01477-0.pdf

Reference37 articles.

1. Shermeyer, J., Van Etten, A.: The effects of super-resolution on object detection performance in satellite imagery. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 0–0 (2019)

2. Yang, D., Li, Z., Xia, Y., Chen, Z.: Remote sensing image super-resolution: challenges and approaches. In: 2015 IEEE International Conference on Digital Signal Processing (DSP), pp. 196–200, IEEE (2015)

3. Anwar, S., Barnes, N.: Densely residual laplacian super-resolution. IEEE Trans. Patt. Anal. Mach. Intell. 44(3), 1192–1204 (2020)

4. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Patt. Anal. Mach. Intell. 38(2), 295–307 (2016). https://doi.org/10.1109/TPAMI.2015.2439281

5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

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