Video Super-Resolution Rankings

More methods: http://www.aas.net.cn/cn/article/doi/10.16383/j.aas.c240235?viewType=HTML

Video Super-Resolution

Method	Frames REDS/Vimeo	Params(M)	REDS4 PSNR	REDS4 SSIM	Vimeo-90K-T PSNR	Vimeo-90K-T SSIM	Vid4 PSNR	Vid4 SSIM
VSRM 1	16/7	17.1	33.11	0.9162	38.33	0.9543	28.44	0.8552
LRTI-VSR 2	16/-	12.9	33.06	0.9162	-	-	-	-
IART 3	16/7	13.4	32.90	0.9138	38.14	0.9528	28.26	0.8517
MIA-VSR 4	16/14	16.5	32.78	0.9220	38.22	0.9532	28.20	0.8507
MFPI 5	-/-	7.3	32.81	0.9106	38.28	0.9534	28.11	0.8481
MambaVSR 6	16/14	14.1	32.75	0.9110	38.33	0.9539	28.20	0.8514
PSRT-recurrent 7	16/14	13.4	32.72	0.9106	38.27	0.9536	28.07	0.8485
DFVSR8	-	7.1	32.76	0.9081	38.25	0.9556	27.92	0.8427
RVRT 9	30/14	10.8	32.75	0.9113	38.15	0.9527	27.99	0.8426
FTVSR++10	-	10.8	32.42	0.907	-	-	28.80	0.868
CTVSR 11	16/14	34.5	32.28	0.9047	-	-	28.03	0.8487
VRT 12	16/7	35.6	32.19	0.9006	38.20	0.9530	27.93	0.8425
TTVSR 13	50/-	6.8	32.12	0.9021	37.92	0.9526	28.40	0.8643
TCNet14	-	9.6	31.82	0.9002	37.94	0.9514	27.48	0.8380
RTA 15	5/7	17.0	31.30	0.8850	37.84	0.9498	27.90	0.8380
BasicVSR++ 16	30/14	7.3	32.39	0.9069	37.79	0.9500	27.79	0.8400
ETDM 17	-	8.4	32.15	0.9024	-	-	28.81	0.8725
FTVSR18	-	10.8	31.82	0.896	-	-	28.31	0.860
MSHPFNL19	-	7.77	-	-	36.75	0.9406	27.70	0.8472
ICNet20	-	18.34	31.71	0.8963	37.72	0.9477	27.43	0.8287
IconVSR 21	15/14	8.7	31.67	0.8948	37.47	0.9476	27.39	0.8279
BasicVSR 21	15/14	6.3	31.42	0.8909	37.18	0.9450	27.24	0.8251
VSR-T 22	5/7	32.6	31.19	0.8815	37.71	0.9494	27.36	0.8258
TGA23	-	5.8	-	-	37.43	0.9480	27.19	0.8213
RLSP24	-	4.2	-	-	37.39	0.9470	27.15	0.8202
EDVR25	5/7	20.6	31.09	0.8800	37.61	0.9489	27.35	0.8264
MSFFN26	-	-	-	-	37.33	0.9467	27.23	0.8218
MuCAN27	5/7	-	30.88	0.8750	37.32	0.9465	-	-
RBPN28	7/7	12.2	30.09	0.8590	37.07	0.9435	27.12	0.8180
PFNL29	7/7	3.0	29.63	0.8502	36.14	0.9363	26.73	0.8029
DUF30	7/7	5.8	28.63	0.8251	-	-	27.33	0.8319
FRVSR31	-	5.1	-	-	-	-	26.69	0.8220
D3Dnet32	-	2.58	-	-	35.65	0.933	26.52	0.799
TOFlow33	5/7	-	27.98	0.7990	33.08	0.9054	25.89	0.7651
3DSRnet 34	-	-	-	-	-	-	25.71	0.7588
SPMC35	-	-	-	-	-	-	25.52	0.76
VESPCN36	-	-	-	-	-	-	25.35	0.7577
VSRNet37	-	0.27	-	-	-	-	22.81	0.65
Bicubic	-	-	26.14	0.7292	31.32	0.8684	23.78	0.6347

Space-Time Video Super-Resolution

https://github.com/AIVFI/Video-Frame-Interpolation-Rankings

Awesome-Super-Resolution

https://github.com/ChaofWang/Awesome-Super-Resolution

Footnotes

1. VSRM: A Robust Mamba-Based Framework for Video Super-Resolution. [ICCV 2025] ↩

2. Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution. [arXiv 2025] ↩

3. An Implicit Alignment for Video Super-Resolution. [CVPR 2024] ↩

4. Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention. [CVPR 2025] ↩

5. Multi-Frequency Representation Enhancement with Privilege Information for Video Super-Resolution. [ICCV 2023] ↩

6. MambaVSR: Content-Aware Scanning State Space Model for Video Super-Resolution. [arXiv 2025] ↩

7. Rethinking Alignment in Video Super-Resolution Transformers. [NeurIPS 2022] ↩

8. DFVSR: Directional Frequency Video Super-Resolution via Asymmetric and Enhancement Alignment Network. [IJCAI 2023] ↩

9. Recurrent Video Restoration Transformer with Guided Deformable Attention. [NeurIPS 2022] ↩

10. Learning Degradation-Robust Spatiotemporal Frequency-Transformer for Video Super-Resolution. [TPAMI 2023] ↩

11. CTVSR: Collaborative Spatial-Temporal Transformer for Video Super-Resolution. [TCSVT 2023] ↩

12. VRT: A Video Restoration Transformer. [TIP 2024] ↩

13. Learning Trajectory-Aware Transformer for Video Super-Resolution. [CVPR 2022] ↩

14. Temporal Consistency Learning of Inter-Frames for Video Super-Resolution. [TCSVT 2023] ↩

15. Revisiting Temporal Alignment for Video Restoration . [CVPR 2022] ↩

16. BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment. [CVPR 2022] ↩

17. Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling. [CVPR 2022] ↩

18. FTVSR: Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution. [ECCV 2022] ↩

19. A Progressive Fusion Generative Adversarial Network for Realistic and Consistent Video Super-Resolution. [TPAMI 2020] ↩

20. ICNet: Joint Alignment and Reconstruction via Iterative Collaboration for Video Super-Resolution. [ACM MM 2022] ↩

21. BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond. [CVPR 2021] ↩ ↩²

22. Video Super-Resolution Transformer. [arxiv 2021] ↩

23. Video Super-resolution with Temporal Group Attention. [CVPR 2020] ↩

24. Efficient Video Super-Resolution through Recurrent Latent Space Propagation. [ICCVW 2019] ↩

25. EDVR: Video Restoration with Enhanced Deformable Convolutional Networks. [CVPRW 2019] ↩

26. Multi-Stage Feature Fusion Network for Video Super-Resolution. [TIP 2021] ↩

27. MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution. [ECCV 2020] ↩

28. Recurrent Back-Projection Network for Video Super-Resolution. [CVPR 2019] ↩

29. Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations. [ICCV 2019] ↩

30. Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation. [CVPR 2018] ↩

31. Frame-Recurrent Video Super-Resolution. [CVPR 2018] ↩

32. Deformable 3D Convolution for Video Super-Resolution. [SPL 2020] ↩

33. Video Enhancement with Task-Oriented Flow. [IJCV 2019] ↩

34. 3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks. [ICIP 2019] ↩

35. Detail-revealing Deep Video Super-resolution. [ICCV 2017] ↩

36. Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation. [CVPR 2017] ↩

37. Video Super-Resolution With Convolutional Neural Networks. [TCI 2016] ↩