[3] S. Bell-Kligler, A. Shocher, and M. Irani. Blind super-resolution kernel estimation using an internal-gan. arXiv preprint arXiv:1909.06581, 2019. 2
[4] N. Bodla, G. Shrivastava, R. Chellappa, and A. Shrivastava. Hierarchical video prediction using relational layouts for human-object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12146-12155, 2021. 1
[5] K. C. Chan, S. Zhou, X. Xu, and C. C. Loy. Basicvsr++: Improving video super-resolution with enhanced propagation and alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5972-5981, 2022. 3, 8, 15
[6] M. Chu, Y. Xie, L. Leal-Taixe, and N. Thuerey. Temporally coherent gans for video superresolution (tecogan). arXiv preprint arXiv:1811.09393, 1(2):3, 2018. 3
[7] E. Denton and R. Fergus. Stochastic video generation with a learned prior. In International Conference on Machine Learning, pages 1174–1183. PMLR, 2018. 4
[8] V. Dewil, J. Anger, A. Davy, T. Ehret, G. Facciolo, and P. Arias. Self-supervised training for blind multi-frame video denoising. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 2724-2734, 2021. 6
[9] A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. Van Der Smagt, D. Cremers, and T. Brox. Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 2758-2766, 2015. 3
[10] Y. Gandelsman, A. Shocher, and M. Irani. "double-dip": Unsupervised image decomposition via coupled deep-image-priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11026-11035, 2019. 2, 5
[11] C. Gao, A. Saraf, J.-B. Huang, and J. Kopf. Flow-edge guided video completion, 2020. 1, 3, 9
[12] M. Haris, G. Shakhnarovich, and N. Ukita. Recurrent back-projection network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3897-3906, 2019. 1, 3
[13] M. Haris, G. Shakhnarovich, and N. Ukita. Space-time-aware multi-resolution video enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 1
[14] Z. Huang, T. Zhang, W. Heng, B. Shi, and S. Zhou. Real-time intermediate flow estimation for video frame interpolation. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 3, 7, 8, 15
[15] E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox. Flownet 2.0: Evolution of optical flow estimation with deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2462-2470, 2017. 3
[16] M. Irani and S. Peleg. Image sequence enhancement using multiple motions analysis. Hebrew University of Jerusalem. Leibniz Center for Research in Computer ..., 1991. 2
[17] H. Jiang, D. Sun, V. Jampani, M.-H. Yang, E. Learned-Miller, and J. Kautz. Super slomo: High quality estimation of multiple intermediate frames for video interpolation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 9000-9008, 2018. 3
[18] Y. Jo, S. W. Oh, J. Kang, and S. J. Kim. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3224-3232, 2018. 1
[19] D. Kim, S. Woo, J.-Y. Lee, and I. S. Kweon. Deep video inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5792-5801, 2019. 2
[20] T. H. Kim, M. S. M. Sajjadi, M. Hirsch, and B. Scholkopf. Spatio-temporal transformer network for video restoration. In Proceedings of the European Conference on Computer Vision (ECCV), September 2018. 3