Dense video super-resolution time-differential network with feature enrichment module
Author:
Funder
the National Natural Science Foundation of China
the Foundation of Fujian Natural Science
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11760-024-03436-2.pdf
Reference41 articles.
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2. Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution, in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6–12, Proceedings, Part IV 13, 2014, pp. 184–199: Springer. (2014)
3. Sun, Y., Qin, J., Gao, X., Chai, S., Chen, B.: Attention-enhanced multi-scale residual network for single image super-resolution. Signal. Image Video Process. 16(5), 1417–1424 (2022)
4. Chen, X., Yang, R., Guo, C.: A lightweight multi-scale residual network for single image super-resolution. Signal. Image Video Process. 16(7), 1793–1801 (2022)
5. Lu, X., Xie, X., Ye, C., Xing, H., Liu, Z., Chen, Y.: Single-image super-resolution via a lightweight convolutional neural network with improved shuffle learning, Signal, Image and Video Processing, pp. 1–9, (2023)
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