[40] K. Soomro, A. R. Zamir, and M. Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 7, 8, 18
[41] C. Studholme, D. J. Hawkes, and D. L. Hill. Normalized entropy measure for multimodality image alignment. In Medical imaging 1998: image processing, volume 3338, pages 132-143. SPIE, 1998. 5
[42] M. Tassano, J. Delon, and T. Veit. Dvdnet: A fast network for deep video denoising. In 2019 IEEE International Conference on Image Processing (ICIP), pages 1805–1809. IEEE, 2019. 1, 3
[43] M. Tassano, J. Delon, and T. Veit. Fastvdnet: Towards real-time deep video denoising without flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 1, 6, 14, 15
[44] Z. Teed and J. Deng. Raft: Recurrent all-pairs field transforms for optical flow, 2020. 3
[45] M. Thomas and A. T. Joy. Elements of information theory. Wiley-Interscience, 2006. 5
[46] D. Ulyanov, A. Vedaldi, and V. Lempitsky. Deep image prior. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 9446–9454, 2018. 2, 5, 9
[47] T. Unterthiner, S. van Steenkiste, K. Kurach, R. Marinier, M. Michalski, and S. Gelly. Towards accurate generative models of video: A new metric & challenges. arXiv preprint arXiv:1812.01717, 2018. 19
[48] Y. Vinker, E. Horwitz, N. Zabari, and Y. Hoshen. Deep single image manipulation. arXiv preprint arXiv:2007.01289, 2020. 2
[49] X. Wang, K. C. K. Chan, K. Yu, C. Dong, and C. C. Loy. Edvr: Video restoration with enhanced deformable convolutional networks, 2019. 1, 8, 15
[50] Y. Wexler, E. Shechtman, and M. Irani. Space-time completion of video. IEEE Transactions on pattern analysis and machine intelligence, 29(3):463-476, 2007. 1
[51] T. Wiegand, E. Steinbach, and B. Girod. Affine multipicture motion-compensated prediction. IEEE Transactions on Circuits and Systems for Video Technology, 15(2):197-209, 2005. 2
[52] R. Xu, X. Li, B. Zhou, and C. C. Loy. Deep flow-guided video inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 3
[53] R. Xu, X. Li, B. Zhou, and C. C. Loy. Deep flow-guided video inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3723-3732, 2019. 9
[54] T. Xue, B. Chen, J. Wu, D. Wei, and W. T. Freeman. Video enhancement with task-oriented flow. International Journal of Computer Vision (IJCV), 127(8):1106-1125, 2019. 1, 3, 7, 8, 15, 18
[55] J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang. Generative image inpainting with contextual attention. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5505-5514, 2018. 2
[56] H. Zhang, L. Mai, N. Xu, Z. Wang, J. Collomosse, and H. Jin. An internal learning approach to video inpainting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2720-2729, 2019. 1, 3, 9
[57] L. P. Zuckerman, E. Naor, G. Pisha, S. Bagon, and M. Irani. Across scales and across dimensions: Temporal super-resolution using deep internal learning. In European Conference on Computer Vision, pages 52–68. Springer, 2020. 3