Fetching the paper…
Reading the bibliography…
Compressed video super-resolution (VSR) aims to restore high-resolution frames from compressed low-resolution counterparts.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE TIP 13
2004
Earlier work this paper cites.
Zhang, L., Zhang, H., Shen, H., Li, P.: A super-resolution reconstruction algorithm for surveillance images. Signal Processing 90
2010
Earlier work this paper cites.
Goto, T., Fukuoka, T., Nagashima, F., Hirano, S., Sakurai, M.: Super-resolution system for 4k-hdtv. In: ICPR. pp. 4453–4458. IEEE (2014)
2014
Earlier work this paper cites.
Wang, Z., Liu, D., Chang, S., Ling, Q., Yang, Y., Huang, T.S.: D3: Deep dual-domain based fast restoration of jpeg-compressed images. In: CVPR. pp. 2764–2772 (2016)
2016
Earlier work this paper cites.
Fu, J., Zheng, H., Mei, T.: Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition. In: CVPR. pp. 4438–4446 (2017)
2017
Earlier work this paper cites.
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep laplacian pyramid networks for fast and accurate super-resolution. In: CVPR. pp. 624–632 (2017)
2017
Earlier work this paper cites.
Tao, X., Gao, H., Liao, R., Wang, J., Jia, J.: Detail-revealing deep video super-resolution. In: ICCV. pp. 4472–4480 (2017)
2017
Earlier work this paper cites.
Zheng, H., Fu, J., Mei, T., Luo, J.: Learning multi-attention convolutional neural network for fine-grained image recognition. In: ICCV. pp. 5209–5217 (2017)
2017
Earlier work this paper cites.
Gueguen, L., Sergeev, A., Kadlec, B., Liu, R., Yosinski, J.: Faster neural networks straight from jpeg. NeurIPS 31
2018
Earlier work this paper cites.
Jo, Y., Oh, S.W., Kang, J., Kim, S.J.: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: CVPR. pp. 3224–3232 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Kim, T.H., Sajjadi, M.S., Hirsch, M., Scholkopf, B.: Spatio-temporal transformer network for video restoration. In: ECCV. pp. 106–122 (2018)
2018
Earlier work this paper cites.
Lu, G., Ouyang, W., Xu, D., Zhang, X., Gao, Z., Sun, M.T.: Deep kalman filtering network for video compression artifact reduction. In: ECCV. pp. 568–584 (2018)
2018
Earlier work this paper cites.
Sajjadi, M.S., Vemulapalli, R., Brown, M.: Frame-recurrent video super-resolution. In: CVPR. pp. 6626–6634 (2018)
2018
Earlier work this paper cites.
Ehrlich, M., Davis, L.S.: Deep residual learning in the jpeg transform domain. In: ICCV. pp. 3484–3493 (2019)
2019
Cited alongside, same era.
Fritsche, M., Gu, S., Timofte, R.: Frequency separation for real-world super-resolution. In: ICCVW. pp. 3599–3608. IEEE (2019)
2019
Cited alongside, same era.
Haris, M., Shakhnarovich, G., Ukita, N.: Recurrent back-projection network for video super-resolution. In: CVPR. pp. 3897–3906 (2019)
2019
Cited alongside, same era.
Li, S., He, F., Du, B., Zhang, L., Xu, Y., Tao, D.: Fast spatio-temporal residual network for video super-resolution. In: CVPR. pp. 10522–10531 (2019)
2019
Cited alongside, same era.
Lu, G., Zhang, X., Ouyang, W., Xu, D., Chen, L., Gao, Z.: Deep non-local kalman network for video compression artifact reduction. TIP 29
2019
Cited alongside, same era.
Li, W., Tao, X., Guo, T., Qi, L., Lu, J., Jia, J.: MuCAN: Multi-correspondence aggregation network for video super-resolution. In: ECCV. pp. 335–351. Springer (2020)
2020
Later among the works it cites.
Tian, Y., Zhang, Y., Fu, Y., Xu, C.: TDAN: Temporally-deformable alignment network for video super-resolution. In: CVPR. pp. 3360–3369 (2020)
2020
Later among the works it cites.
Xu, K., Qin, M., Sun, F., Wang, Y., Chen, Y.K., Ren, F.: Learning in the frequency domain. In: CVPR. pp. 1740–1749 (2020)
2020
Later among the works it cites.
Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.: Learning texture transformer network for image super-resolution. In: CVPR. pp. 5791–5800 (2020)
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Nah, S., Baik, S., Hong, S., Moon, G., Son, S., Timofte, R., Mu Lee, K.: Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study. In: CVPRW. pp. 0–0 (2019)
2019
Cited alongside, same era.
Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: EDVR: Video restoration with enhanced deformable convolutional networks. In: CVPRW (2019)
2019
Cited alongside, same era.
Xu, Y., Gao, L., Tian, K., Zhou, S., Sun, H.: Non-local convlstm for video compression artifact reduction. In: ICCV. pp. 7043–7052 (2019)
2019
Cited alongside, same era.
Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. IJCV 127
2019
Cited alongside, same era.
Chu, M., Xie, Y., Mayer, J., Leal-Taixé, L., Thuerey, N.: Learning temporal coherence via self-supervision for gan-based video generation. ACM TOG 39
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Ehrlich, M., Davis, L., Lim, S.N., Shrivastava, A.: Quantization guided jpeg artifact correction. In: ECCV. pp. 293–309. Springer (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
Chan, K.C., Wang, X., Yu, K., Dong, C., Loy, C.C.: BasicVSR: The search for essential components in video super-resolution and beyond. In: CVPR. pp. 4947–4956 (2021)
2021
Later among the works it cites.
Li, X., Jin, X., Yu, T., Sun, S., Pang, Y., Zhang, Z., Chen, Z.: Learning omni-frequency region-adaptive representations for real image super-resolution. In: AAAI. vol. 35, pp. 1975–1983 (2021)
2021
Later among the works it cites.
Li, Y., Jin, P., Yang, F., Liu, C., Yang, M.H., Milanfar, P.: COMISR: Compression-informed video super-resolution. In: ICCV (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: ICCV. pp. 10012–10022 (2021)
2021
Later among the works it cites.
Qin, Z., Zhang, P., Wu, F., Li, X.: FcaNet: Frequency channel attention networks. In: ICCV. pp. 783–792 (2021)
2021
Later among the works it cites.
Yi, P., Wang, Z., Jiang, K., Jiang, J., Lu, T., Tian, X., Ma, J.: Omniscient video super-resolution. In: ICCV. pp. 4429–4438 (2021)
2021
Later among the works it cites.
Zeng, Y., Yang, H., Chao, H., Wang, J., Fu, J.: Improving visual quality of image synthesis by a token-based generator with transformers. NeurIPS 34
2021
Later among the works it cites.
Liu, C., Yang, H., Fu, J., Qian, X.: Learning trajectory-aware transformer for video super-resolution. In: CVPR. pp. 5687–5696 (2022)
2022
Closest in time.