Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Software
Reference48 articles.
1. Bell-Kligler, S., Shocher, A., & Irani, M. (2019). Blind super-resolution kernel estimation using an internal-gan. In NeurIPS (pp. 284–293).
2. Bevilacqua, M., Roumy, A., Guillemot, C., & Alberi-Morel, M. L. (2012). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In BMVC (pp. 1–10).
3. Bulat, A., Yang, J., & Tzimiropoulos, G. (2018). To learn image super-resolution, use a GAN to learn how to do image degradation first. In ECCV (pp. 187–202).
4. Chen, X., Zhang, J., Xu, C., Wang, Y., Wang, C., & Liu, Y. (2023). Better ”CMOS” produces clearer images: Learning space-variant blur estimation for blind image super-resolution. In CVPR (pp. 1651–1661).
5. Chen, X., Zhang, J., Xu, C., Wang, Y., Wang, C., & Liu, Y. (2023). Better "CMOS" produces clearer images: Learning space-variant blur estimation for blind image super-resolution. In CVPR (pp. 1651–1661).
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献