Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Reference43 articles.
1. Ahn, N., Kang, B., & Sohn, KA .(2018). Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European conference on computer vision (ECCV).
2. Antoni, B., Joan, D., & Julia, N. (2019). Motion-compensated Spatio-temporal filtering for multi-image and multimodal super-resolution. International Journal of Computer Vision, 127(10), 1474–1500.
3. Bevilacqua, M., Roumy, A., Guillemot, C., & Alberi-Morel, ML. (2012). Low-complexity single-image super-resolution based on nonnegative neighbor embedding pp. 1–10.
4. Bulat, A., & Tzimiropoulos, G. (2019). Xnor-net++: Improved binary neural networks. arXiv preprint arXiv:1909.13863.
5. Chen, TQ., Rubanova, Y., Bettencourt, J., & Duvenaud, DK. (2018). Neural ordinary differential equations. In: Advances in neural information processing systems, pp. 6571–6583.
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