Deep Video Super-Resolution

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1) The state-of-the-art VSR

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Based on paperwithcode VSR task, this repository contains summary of the state-of-the-art VSR methods.

The state-of-the-art VSR

Model	Published	Code	Year	BI degradation Vid4 Y - 4x (PSNR)
PSRT	NeurIPS22	PyTorch	2022	28.07
RVRT	NeurIPS22	PyTorch	2022	27.99
VRT	arXiv	PyTorch	2022	27.93
BasicVSR++	CVPR22	PyTorch	2022	27.79
RRN-L	arXiv	PyTorch	2020	27.69
iSeeBetter	Computational Visual Media	PyTorch	2020	27.43
PFNL	ICCV19	TensorFlow	2019	27.40
IconVSR	CVPR21	PyTorch	2021	27.39
ADNLVSR	Neurocomputing	-	2020	27.39
EDVR	CVPR19	PyTorch	2019	27.35
VSR-DUF	CVPR18	TensorFlow	2018	27.31
BasicVSR	CVPR21	PyTorch	2021	27.24
RBPN/6-PF	CVPR19	PyTorch	2019	27.12
TDAN	CVPR20	PyTorch	2020	26.86
FRVSR	CVPR18	-	2018	26.69
WDVR	CVPR19	PyTorch	2019	26.62
MDCN	Neurocomputing	-	2019	26.49
DDAN	IEEE Transactions on Image Processing	-	2020	26.48
SOF-VSR	IEEE Transactions on Image Processing	PyTorch	2020	26.01
DRDVSR	ICCV17	TensorFlow	2017	25.88
VESPCN	CVPR17	-	2017	25.35
Bicubic (Baseline)				23.82

• PSRT

• RVRT

• VRT

• BasicVSR++

• RRN-L

• iSeeBetter

• PFNL

• IconVSR

• ADNLVSR

• EDVR

• VSR-DUF

• BasicVSR

• RBPN/6-PF

• TDAN

• FRVSR

• WDVR

• MDCN

• DDAN

• SOF-VSR

• DRDVSR

• VESPCN

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2) The datasets of VSR

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Please refer to Dataset.md for more details.

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Citation

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• Shi, Shuwei, et al. "Rethinking alignment in video super-resolution transformers." arXiv preprint arXiv:2207.08494 (2022).
• Liang, Jingyun, et al. "Recurrent Video Restoration Transformer with Guided Deformable Attention." arXiv preprint arXiv:2206.02146 (2022).
• Liang, Jingyun, et al. "Vrt: A video restoration transformer." arXiv preprint arXiv:2201.12288 (2022).
• Chan, Kelvin CK, et al. "BasicVSR++: Improving video super-resolution with enhanced propagation and alignment." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
• Isobe, Takashi, Fang Zhu, and Shengjin Wang. "Revisiting Temporal Modeling for Video Super-resolution." arXiv preprint arXiv:2008.05765 (2020).
• Chadha, Aman, John Britto, and M. Mani Roja. "iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks." Computational Visual Media (2020): 1-12.
• Yi, Peng, et al. "Progressive fusion video super-resolution network via exploiting non-local spatio-temporal correlations." Proceedings of the IEEE International Conference on Computer Vision. 2019.
• Chan, Kelvin CK, et al. "BasicVSR: The search for essential components in video super-resolution and beyond." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
• Sun, Wei, and Yanning Zhang. "Attention-guided Dual Spatial-Temporal Non-local Network for Video Super-Resolution." Neurocomputing (2020).
• Wang, Xintao, et al. "Edvr: Video restoration with enhanced deformable convolutional networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019.
• Jo, Younghyun, et al. "Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
• Haris, Muhammad, Gregory Shakhnarovich, and Norimichi Ukita. "Recurrent back-projection network for video super-resolution." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
• Tian, Yapeng, et al. "TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.
• Sajjadi, Mehdi SM, Raviteja Vemulapalli, and Matthew Brown. "Frame-recurrent video super-resolution." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
• Fan, Yuchen, et al. "An Empirical Investigation of Efficient Spatio-Temporal Modeling in Video Restoration." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2019.
• Purohit, Kuldeep, Srimanta Mandal, and A. N. Rajagopalan. "Mixed-dense connection networks for image and video super-resolution." Neurocomputing (2019).
• Li, Feng, Huihui Bai, and Yao Zhao. "Learning a Deep Dual Attention Network for Video Super-Resolution." IEEE Transactions on Image Processing 29 (2020): 4474-4488.
• Wang, Longguang, et al. "Deep Video Super-Resolution using HR Optical Flow Estimation." arXiv preprint arXiv:2001.02129 (2020).
• Tao, Xin, et al. "Detail-revealing deep video super-resolution." Proceedings of the IEEE International Conference on Computer Vision. 2017.
• Caballero, Jose, et al. "Real-time video super-resolution with spatio-temporal networks and motion compensation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.