An Efficient and Lightweight Structure for Spatial-Temporal Feature Extraction in Video Super Resolution
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50069-5_30
Reference23 articles.
1. Bao, W., Lai, W.S., Zhang, X., Gao, Z., Yang, M.H.: MEMC-Net: motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 933–948 (2019)
2. Caballero, J., et al.: Real-time video super-resolution with spatio-temporal networks and motion compensation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4778–4787 (2017)
3. Cao, J., et al.: Towards interpretable video super-resolution via alternating optimization. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol. 13678, pp. 393–411. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19797-0_23
4. Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: Understanding deformable alignment in video super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp. 973–981 (2021)
5. Chiche, B.N., Woiselle, A., Frontera-Pons, J., Starck, J.L.: Stable long-term recurrent video super-resolution. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 837–846 (2022)
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