Fetching the paper…
Reading the bibliography…
Most video super-resolution methods super-resolve a single reference frame with the help of neighboring frames in a temporal sliding window.
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: AISTATS (2011)
2011
Earlier work this paper cites.
Liu, C., Sun, D.: On bayesian adaptive video super resolution. IEEE transactions on pattern analysis and machine intelligence 36
2013
Earlier work this paper cites.
Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: ECCV (2014)
2014
Earlier work this paper cites.
Huang, Y., Wang, W., Wang, L.: Bidirectional recurrent convolutional networks for multi-frame super-resolution. In: NeurIPS (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)
2015
Earlier work this paper cites.
Jia, X., De Brabandere, B., Tuytelaars, T., Gool, L.V.: Dynamic filter networks. In: NeurIPS (2016)
2016
Earlier work this paper cites.
Kappeler, A., Yoo, S., Dai, Q., Katsaggelos, A.K.: Video super-resolution with convolutional neural networks. IEEE Transactions on Computational Imaging 2
2016
Earlier work this paper cites.
Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR (2016)
2016
Earlier work this paper cites.
Kim, J., Lee, J.K., Lee, K.M.: Deeply-recursive convolutional network for image super-resolution. In: CVPR (2016)
2016
Earlier work this paper cites.
Singh, B., Marks, T.K., Jones, M., Tuzel, O., Shao, M.: A multi-stream bi-directional recurrent neural network for fine-grained action detection. In: CVPR (2016)
2016
Earlier work this paper cites.
Caballero, J., Ledig, C., Aitken, A., Acosta, A., Totz, J., Wang, Z., Shi, W.: Real-time video super-resolution with spatio-temporal networks and motion compensation. In: CVPR (2017)
2017
Earlier work this paper cites.
Du, W., Wang, Y., Qiao, Y.: Rpan: An end-to-end recurrent pose-attention network for action recognition in videos. In: CVPR (2017)
2017
Cited alongside, same era.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)
2017
Cited alongside, same era.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)
2017
Cited alongside, same era.
Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: CVPR Workshops (2017)
2017
Cited alongside, same era.
Liu, D., Wang, Z., Fan, Y., Liu, X., Wang, Z., Chang, S., Huang, T.: Robust video super-resolution with learned temporal dynamics. In: ICCV (2017)
Sajjadi, M.S., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: CVPR (2018)
2018
Later among the works it cites.
Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: ECCV (2018)
2018
Later among the works it cites.
2018
Later among the works it cites.
2019
Later among the works it cites.
Haris, M., Shakhnarovich, G., Ukita, N.: Recurrent back-projection network for video super-resolution. In: CVPR (2019)
2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Tao, X., Gao, H., Liao, R., Wang, J., Jia, J.: Detail-revealing deep video super-resolution. In: ICCV (2017)
2017
Cited alongside, same era.
Haris, M., Shakhnarovich, G., Ukita, N.: Deep back-projection networks for super-resolution. In: CVPR (2018)
2018
Cited alongside, same era.
Jo, Y., Wug Oh, S., Kang, J., Joo Kim, S.: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: CVPR (2018)
2018
Cited alongside, same era.
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE transactions on pattern analysis and machine intelligence 41
2018
Cited alongside, same era.
Pan, J., Liu, S., Sun, D., Zhang, J., Liu, Y., Ren, J., Li, Z., Tang, J., Lu, H., Tai, Y.W., et al.: Learning dual convolutional neural networks for low-level vision. In: CVPR (2018)
2018
Cited alongside, same era.
Later among the works it cites.
Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: Edvr: Video restoration with enhanced deformable convolutional networks. In: CVPR Workshops (2019)
2019
Later among the works it cites.
Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. International Journal of Computer Vision 127
2019
Later among the works it cites.
Yang, W., Zhang, X., Tian, Y., Wang, W., Xue, J.H., Liao, Q.: Deep learning for single image super-resolution: A brief review. IEEE Transactions on Multimedia 21
2019
Later among the works it cites.
Yi, P., Wang, Z., Jiang, K., Jiang, J., Ma, J.: Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations. In: ICCV (2019)
2019
Later among the works it cites.
Isobe, T., Li, S., Jia, X., Yuan, S., Slabaugh, G., Xu, C., Li, Y.L., Wang, S., Tian, Q.: Video super-resolution with temporal group attention. In: CVPR (2020)
2020
Closest in time.