基于深度学习的视频超分辨率重建算法进展

唐麒; 赵耀; 刘美琴; 姚超

doi:10.16383/j.aas.c240235

基于深度学习的视频超分辨率重建算法进展

doi: 10.16383/j.aas.c240235 cstr: 32138.14.j.aas.c240235

唐麒^{1, 2,},
赵耀^{1, 2,},
刘美琴^{1, 2,},
姚超^3,

1.
北京交通大学信息科学研究所北京 100044
2.
北京交通大学视觉智能交叉创新教育部国际合作联合实验室北京 100044
3.
北京科技大学计算机与通信工程学院北京 100083

基金项目: 中央高校基本科研业务费专项资金(2024JBZX001), 国家自然科学基金(62120106009, 62332017, 62372036) 资助

详细信息

作者简介:
唐麒：北京交通大学信息科学研究所硕士研究生. 主要研究方向为图像与视频复原. E-mail: qitang@bjtu.edu.cn

赵耀：北京交通大学信息科学研究所教授. 主要研究方向为图像/视频压缩, 数字媒体内容安全, 媒体内容分析与理解, 人工智能. E-mail: yzhao@bjtu.edu.cn

刘美琴：北京交通大学信息科学研究所教授. 主要研究方向为多媒体信息处理, 三维视频处理, 视频智能编码. 本文通信作者. E-mail: mqliu@bjtu.edu.cn

姚超：北京科技大学计算机与通信工程学院副教授. 主要研究方向为图像/视频压缩, 计算机视觉和人机交互. E-mail: yaochao@ustb.edu.cn

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出版历程
- 收稿日期: 2024-04-29
- 录用日期: 2024-10-16
- 网络出版日期: 2025-03-06

A Review of Video Super-resolution Algorithms Based on Deep Learning

TANG Qi^{1, 2
,},
ZHAO Yao^{1, 2
,},
LIU Mei-Qin^{1, 2
,},
YAO Chao^3
,

1.
Institute of Information Science, Beijing Jiaotong University, Beijing 100044
2.
Visual Intelligence + X International Cooperation Joint Laboratory of Ministry of Education, Beijing Jiaotong University, Beijing 100044
3.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083

Funds: Supported by Fundamental Research Funds for the Central Universities (2024JBZX001) and National Natural Science Foundation of China (62120106009, 62332017, 62372036)

More Information

Author Bio:
TANG Qi 　Master student at the Institute of Information Science, Beijing Jiaotong University. His research interest covers image and video restoration

ZHAO Yao 　Professor at the Institute of Information Science, Beijing Jiaotong University. His research interest covers image/video compression, digital media content security, media content analysis and understanding, and artificial intelligence

LIU Mei-Qin 　Professor at the Institute of Information Science, Beijing Jiaotong University. Her research interest covers multimedia information processing, 3D video processing, and video intelligent coding. Corresponding author of this paper

YAO Chao 　Associate professor at the School of Computer and Communication Engineering, University of Science and Technology Beijing. His research interest covers image/video compression, computer vision, and human-computer interaction

摘要

摘要: 视频超分辨率重建(Video super-resolution, VSR)是底层计算机视觉任务中的一个重要研究方向, 旨在利用低分辨率视频的帧内和帧间信息, 重建具有更多细节和内容一致的高分辨率视频, 有助于提升下游任务性能和改善用户观感体验. 近年来, 基于深度学习的视频超分辨率重建算法大量涌现, 在帧间对齐、信息传播等方面取得突破性的进展. 在简述视频超分辨率重建任务的基础上, 梳理现有的视频超分辨率重建的公共数据集及相关算法; 接着, 详细综述基于深度学习的视频超分辨率重建算法的创新性工作进展情况; 最后, 总结视频超分辨率重建算法面临的挑战及未来的发展趋势.
- 视频超分辨率重建 /
- 深度学习 /
- 循环神经网络 /
- 注意力机制 /
- 光流估计 /
- 可变形卷积
Abstract: Video super-resolution (VSR) is an essential research realm within low-level vision tasks. It aims to reconstruct high-resolution video with realistic details and coherent content by utilizing intra-frame and inter-frame information of low-resolution video, which positively impacts the performance of downstream tasks and the improvement of user's perception experience. In recent years, VSR base on deep learning has emerged abundantly, make breakthrough progress in inter-frame alignment, information propagation, and other aspeets. On the basis of briefly describing the task of VSR, the existing VSR public datasets and related algorithms are combed. Subsequently, the innovative work progress of deep-learning-based VSR are reviewed in detail. Finally, the challenges and future development trends of VSR algorithms are outlined.
- Video super-resolution (VSR) /
- deep learning /
- recurrent neural network /
- attention /
- optical flow estimation /
- deformable convolution

HTML全文

图 1 视频超分辨率重建数据集REDS和Vimeo-90K示例

Fig. 1 Examples of video super-resolution datasets from REDS and Vimeo-90K

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图 2 部分VSR模型在REDS据集的可视化比较结果

Fig. 2 Visual comparison results of some VSR models on REDS dataset

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图 3 部分VSR模型在Vid4数据集的可视化比较结果

Fig. 3 Visual comparison results of some VSR models on Vid4 dataset

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图 4 本文结构图

Fig. 4 Architecture of the paper

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图 5 基于深度学习的视频超分辨率重建时间脉络图

Fig. 5 Timeline of video super-resolution based on deep learning

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图 6 VSRNet结构图

Fig. 6 Architecture of VSRNet

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图 7 VESPCN结构图

Fig. 7 Architecture of VESPCN

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图 8 SOFVSR结构图

Fig. 8 Architecture of SOFVSR

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图 9 TOFlow结构图

Fig. 9 Architecture of TOFlow

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图 10 DUF结构图

Fig. 10 Architecture of DUF

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图 11 FSTRN结构图

Fig. 11 Architecture of FSTRN

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图 12 TDAN结构图

Fig. 12 Architecture of TDAN

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图 13 EDVR结构图

Fig. 13 Architecture of EDVR

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图 14 TGA结构图

Fig. 14 Architecture of TGA

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图 15 MuCAN结构图

Fig. 15 Architecture of MuCAN

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图 16 MANA结构图

Fig. 16 Architecture of MANA

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图 17 IAM结构图

Fig. 17 Architecture of IAM

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图 18 VSR Transformer结构图

Fig. 18 Architecture of VSR Transformer

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图 19 VRT结构图

Fig. 19 Architecture of VRT

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图 20 DRVSR结构图

Fig. 20 Architecture of DRVSR

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图 21 FRVSR结构图

Fig. 21 Architecture of FRVSR

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图 22 RBPN结构图

Fig. 22 Architecture of RBPN

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图 23 RLSP结构图

Fig. 23 Architecture of RLSP

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图 24 RSDN结构图

Fig. 24 Architecture of RSDN

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图 25 RRN结构图

Fig. 25 Architecture of RRN

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图 26 DAP结构图

Fig. 26 Architecture of DAP

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图 27 ETDM结构图

Fig. 27 Architecture of ETDM

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图 28 TMP结构图

Fig. 28 Architecture of TMP

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图 29 BRCN结构图

Fig. 29 Architecture of BRCN

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图 30 RRCN结构图

Fig. 30 Architecture of RRCN

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图 31 PFNL结构图和PFRB细节图

Fig. 31 Architecture of PFNL and detail of PFRB

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图 32 RISTN结构图

Fig. 32 Architecture of RISTN

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图 33 LOVSR和GOVSR结构图

Fig. 33 Architectures of LOVSR and GOVSR

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图 34 BasicVSR和ICONVSR结构图

Fig. 34 Architectures of BasicVSR and ICONVSR

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图 35 TTVSR结构图

Fig. 35 Architecture of TTVSR

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图 36 CTVSR结构图

Fig. 36 Architecture of CTVSR

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图 37 RefVSR结构图

Fig. 37 Architecture of RefVSR

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图 38 C²-Mathching结构图

Fig. 38 Architecture of C²-Mathching

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图 39 RealBasicVSR结构图

Fig. 39 Architecture of RealBasicVSR

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图 40 FTVSR结构图

Fig. 40 Architecture of FTVSR

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图 41 BasicVSR++ 结构图

Fig. 41 Architecture of BasicVSR++

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图 42 PSRT结构图

Fig. 42 Architecture of PSRT

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图 43 IART结构图

Fig. 43 Architecture of IART

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图 44 MFPI结构图

Fig. 44 Architecture of MFPI

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图 45 DFVSR结构图

Fig. 45 Architecture of DFVSR

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图 46 MIA-VSR结构图

Fig. 46 Architecture of MIA-VSR

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图 47 RVRT结构图

Fig. 47 Architecture of RVRT

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图 48 TeoGAN结构图

Fig. 48 Architecture of TeoGAN

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图 49 StableVSR结构图

Fig. 49 Architecture of StableVSR

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图 50 MGLD结构图

Fig. 50 Architecture of MGLD

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图 51 Upscale-A-Video结构图

Fig. 51 Architecture of Upscale-A-Video

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图 52 不同帧间对齐模式示意图

Fig. 52 Illustration of different inter-frame alignment

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图 53 基于光流的显式运动对齐

Fig. 53 Explicit alignment based on optical flow

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图 54 基于可变形卷积的对齐

Fig. 54 Deformable convolution-based alignment

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图 55 光流引导的可变形对齐和光流引导的可变形注意力

Fig. 55 Flow-guided deformable alignment and flow-guided deformable attention

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图 56 基于3D卷积的帧间对齐

Fig. 56 Inter-frame alignment based on 3D convolution

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表 1 基于深度学习的视频超分辨率重建数据集

Table 1 Datasets of video super-resolution based on deep learning

数据集			类型	视频数量	帧数	分辨率 (像素)	颜色空间
合成数据集	YUV25^[15]		训练集	25	—	386 × 288	YUV
	TDTFF^[16]	Turbine	测试集	5	—	648 × 528	YUV
		Dancing				950 × 530
		Treadmill				700 × 600
		Flag				1000 × 580
		Fan				990 × 740
	Vid4^[13]	Foliage	测试集	4	49	720 × 480	RGB
		Walk			47	720 × 480
		Calendar			41	720 × 576
		City			34	704 × 576
	YUV21^[17]		测试集	21	100	352 × 288	YUV
	Venice^[18]		训练集	1	1 077	3 840 × 2 160	RGB
	Myanmar^[19]		训练集	1	527	3 840 × 2 160	RGB
	CDVL^[20]		训练集	100	30	1 920 × 1 080	RGB
	UVGD^[21]		测试集	16	—	3 840 × 2 160	YUV
	LMT^[22]		训练集	26	—	1 920 × 1 080	YCbCr
	SPMCS^[23]		训练集和测试集	975	31	960 × 540	RGB
	MM542^[24]		训练集	542	32	1 280 × 720	RGB
	UDM10^[25]		测试集	10	32	1 272 × 720	RGB
	Vimeo-90K^[12]		训练集和测试集	91 701	7	448 × 256	RGB
	REDS^[14]		训练集和测试集	270	100	1 280 × 720	RGB
	Parkour^[26]		测试集	14	—	960 × 540	RGB
真实数据集	RealVSR^[27]		训练集和测试集	500	50	1 024 × 512	RGB/YCbCr
	VideoLQ^[28]		测试集	50	100	1 024 × 512	RGB
	RealMCVSR^[29]		训练集和测试集	161	—	1 920 × 1 080	RGB
	MVSR4$ \times $^[30]		训练集和测试集	300	100	1 920 × 1 080	RGB
	DTVIT^[31]		训练集和测试集	196	100	1 920 × 1 080	RGB
	YouHQ^[32]		训练集和测试集	38 616	32	1 920 × 1 080	RGB

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表 2 对双三次插值下采样后的视频进行VSR的性能(PSNR/SSSIM)对比结果

Table 2 Performance (PSNR/SSSIM) comparison of video super-resolution algorithm with bicubic downsampling

对比方法	训练帧数	参数量(M)	双三次插值下采样
对比方法	训练帧数	参数量(M)	REDS (RGB通道)	Vimeo-90K-T (Y通道)	Vid4 (Y通道)
Bicubic	—	—	26.14/0.7292	31.32/0.8684	23.78/0.6347
VSRNet^[40]	—	0.27	−/−	−/−	22.81/0.6500
VSRResFeatGAN^[41]	—	—	−/−	−/−	24.50/0.7023
VESPCN^[42]	—	—	−/−	−/−	25.35/0.7577
VSRResNet^[41]	—	—	−/−	−/−	25.51/0.7530
SPMC^[23]	—	2.17	−/−	−/−	25.52/0.7600
3DSRNet^[43]	—	—	−/−	−/−	25.71/0.7588
RRCN^[44]	—	—	−/−	−/−	25.86/0.7591
TOFlow^[12]	5/7	1.41	27.98/0.7990	33.08/0.9054	25.89/0.7651
STARNet^[45]	—	111.61	−/−	30.83/0.9290	−/−
MEMC-Net^[46]	—	—	−/−	33.47/0.9470	24.37/0.8380
STMN^[47]	—	—	−/−	−/−	25.90/0.7878
SOFVSR^[48]	—	1.71	−/−	−/−	26.01/0.7710
RISTN^[49]	—	3.67	−/−	−/−	26.13/0.7920
MMCNN^[24]	—	10.58	−/−	−/−	26.28/0.7844
RTVSR^[50]	—	15.00	−/−	−/−	26.36/0.7900
TDAN^[51]	—	1.97	−/−	−/−	26.42/0.7890
D3DNet^[52]	−/7	2.58	−/−	35.65/0.9330	26.52/0.7990
FFCVSR^[53]	—	—	−/−	−/−	26.97/0.8300
EVSRNet^[54]	—	—	27.85/0.8000	−/−	−/−
StableVSR^[55]	—	—	27.97/0.8000	−/−	−/−
DUF^[56]	7/7	5.8	28.63/0.8251	−/−	27.33/0.8319
PFNL^[57]	7/7	3	29.63/0.8502	36.14/0.9363	26.73/0.8029
DNSTNet^[58]	—	—	−/−	36.86/0.9387	27.21/0.8220
RBPN^[59]	7/7	12.2	30.09/0.8590	37.07/0.9435	27.12/0.8180
DSMC^[60]	—	11.58	30.29/0.8381	−/−	27.29/0.8403
Boosted EDVR^[31]	—	—	30.53/0.8699	−/−	−/−
TMP^[61]	—	3.1	30.67/0.8710	−/−	27.10/0.8167
MuCAN^[62]	5/7	—	30.88/0.8750	37.32/0.9465	−/−
MSFFN^[63]	—	—	−/−	37.33/0.9467	27.23/0.8218
DAP^[64]	15/5	—	30.59/0.8703	−/−	−/−
MultiBoot VSR^[65]	—	60.86	31.00/0.8822	−/−	−/−
SSL-bi^[66]	15/14	1.0	31.06/0.8933	36.82/0.9419	27.15/0.8208
EDVR^[67]	5/7	20.6	31.09/0.8800	37.61/0.9489	27.35/0.8264
RLSP^[68]	—	4.2	−/−	37.39/0.9470	27.15/0.8202
TGA^[69]	—	5.8	−/−	37.43/0.9480	27.19/0.8213
KSNet-bi^[70]	—	3.0	31.14/0.8862	37.54/0.9503	27.22/0.8245
VSR-T^[71]	5/7	32.6	31.19/0.8815	37.71/0.9494	27.36/0.8258
PSRT-sliding^[72]	5/−	14.8	31.32/0.8834	−/−	−/−
SeeClear^[73]	5/5	229.23	31.32/0.8856	37.64/0.9503	27.80/0.840 4
DPR^[74]	—	6.3	31.38/0.8907	37.11/0.9446	27.19/0.824 3
BasicVSR^[75]	15/14	6.3	31.42/0.8909	37.18/0.9450	27.24/0.825 1
Boosted BasicVSR^[31]	—	—	31.42/0.8917	−/−	−/−
SATeCo^[76]	6/6	—	31.62/0.8932	−/−	27.44/0.842 0
IconVSR^[75]	15/14	8.7	31.67/0.8948	37.47/0.9476	27.39/0.8279
ICNet^[77]	—	18.34	31.71/0.8963	37.72/0.9477	27.43/0.8287
MSHPFNL^[78]	—	7.77	−/−	36.75/0.9406	27.70/0.8472
PA^[79]	5/7	38.2	32.05/0.8941	−/−	28.02/0.8373
FTVSR^[80]	—	10.8	31.82/0.8960	−/−	−/−
$ C^2 $-Matching^[81]	—	—	32.05/0.9010	−/−	28.87/0.8960
ETDM^[82]	—	8.4	32.15/0.9024	−/−	−/−
BasicVSR++^[83]	30/14	7.3	32.39/0.9069	37.79/0.9500	27.79/0.8400
RTA^[84]	5/7	17	31.30/0.8850	37.84/0.9498	27.90/0.8380
Semantic Lens^[85]	5/−	—	31.42/0.8881	−/−	−/−
TCNet^[86]	—	9.6	31.82/0.9002	37.94/0.9514	27.48/0.8380
TTVSR^[87]	50/−	6.8	32.12/0.9021	−/−	−/−
VRT^[88]	16/7	35.6	32.19/0.9006	38.20/0.9530	27.93/0.8425
CTVSR^[89]	16/14	34.5	32.28/0.9047	−/−	28.03/0.8487
FTVSR++^[90]	—	10.8	32.42/0.9070	−/−	−/−
LGDFNet-BPP^[91]	—	9.0	32.53/0.9007	−/−	27.99/0.8409
PP-MSVSR-L^[92]	—	7.4	32.53/0.9083	−/−	−/−
CFD-BasicVSR++^[127]	30/7	7.5	32.51/0.9083	37.90/0.9504	27.84/0.8406
RVRT^[93]	30/14	10.8	32.75/0.9113	38.15/0.9527	27.99/0.8426
DFVSR^[94]	—	7.1	32.76/0.9081	38.25/0.9556	27.92/0.8427
PSRT-recurrent^[72]	16/14	13.4	32.72/0.9106	38.27/0.9536	28.07/0.8485
MFPI^[95]	−/−	7.3	32.81/0.9106	38.28/0.9534	28.11/0.8481
EvTexture^[96]	15/−	8.9	32.79/0.9174	38.23/0.9544	29.51/0.8909
MIA-VSR^[97]	16/14	16.5	32.78/0.9220	38.22/0.9532	28.20/0.8507
CFD-PSRT^[127]	30/7	13.6	32.83/0.9140	38.33/0.9548	28.18/0.8503
IART^[98]	16/7	13.4	32.90/0.9138	38.14/0.9528	28.26/0.8517
EvTexture+^[96]	15/−	10.1	32.93/0.9195	38.32/0.9558	29.78/0.8983

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表 3 对高斯模糊下采样后的视频进行VSR的性能(PSNR/SSSIM)对比结果

Table 3 Performance (PSNR/SSSIM) comparison of video super-resolution algorithm with blur downsampling

对比方法	训练帧数	参数量(M)	高斯模糊下采样
对比方法	训练帧数	参数量(M)	UDM10 (Y通道)	Vimeo-90K-T (Y通道)	Vid4 (Y通道)
Bicubic	—	—	28.47/0.8253	31.30/0.8687	21.80/0.5246
BRCN^[99]	—	—	−/−	−/−	24.43/0.6334
ToFNet^[12]	5/7	1.41	36.26/0.9438	34.62/0.9212	25.85/0.7659
TecoGAN^[100]	—	3.00	−/−	−/−	25.89/−
SOFVSR^[48]	—	1.71	−/−	−/−	26.19/0.7850
RRN^[101]	—	3.4	38.96/0.9644	−/−	27.69/0.8488
TDAN^[51]	—	1.97	−/−	−/−	26.86/0.8140
FRVSR^[102]	—	5.1	−/−	−/−	26.69/0.8220
DUF^[56]	7/7	5.8	38.48/0.9605	36.87/0.9447	27.38/0.8329
RLSP^[68]	—	4.2	38.48/0.9606	36.49/0.9403	27.48/0.8388
PFNL^[57]	7/7	3	38.74/0.9627	−/−	27.16/0.8355
RBPN^[59]	7/7	12.2	38.66/0.9596	37.20/0.9458	27.17/0.8205
TMP^[61]	—	3.1	−/−	37.33/0.9481	27.61/0.8428
TGA^[69]	—	5.8	38.74/0.9627	37.59/0.9516	27.63/0.8423
SSL-bi^[66]	15/14	1.0	39.35/0.9665	37.06/0.9458	27.56/0.8431
RSDN^[103]	—	6.19	−/−	37.23/0.9471	27.02/0.8505
DAP^[64]	15/5	—	39.50/0.9664	37.25/0.9472	−/−
SeeClear^[73]	5/5	229.23	39.72/0.9675	−/−	−/−
EDVR^[67]	5/7	20.6	39.89/0.9686	37.81/0.9523	27.85/0.8503
DPR^[74]	—	6.3	39.72/0.9684	37.24/0.9461	27.89/0.8539
BasicVSR^[75]	15/14	6.3	39.96/0.9694	37.53/0.9498	27.96/0.8553
IconVSR^[75]	15/14	8.7	40.03/0.9694	37.84/0.9524	28.04/0.8570
R2D2^[104]	—	8.25	39.53/0.9670	−/−	28.13/0.9244
FTVSR^[80]	—	10.8	−/−	−/−	28.31/0.8600
FDAN^[105]	—	—	39.91/0.9686	37.75/0.9522	27.88/0.8508
PP-MSVSR^[92]	—	1.45	40.06/0.9699	37.54/0.9499	28.13/0.8604
GOVSR^[106]	—	—	40.14/0.9713	37.63/0.9503	28.41/0.8724
ETDM^[82]	—	8.4	40.11/0.9707	−/−	28.81/0.8725
TTVSR^[87]	50/−	6.8	40.41/0.9712	37.92/0.9526	28.40/0.8643
BasicVSR++^[83]	30/14	7.3	40.72/0.9722	38.21/0.9550	29.04/0.8753
CFD-BasicVSR++^[127]	30/7	7.5	40.77/0.9726	38.36/0.9557	29.14/0.8760
TCNet^[86]	—	9.6	−/−	−/−	28.44/0.8730
VRT^[88]	16/7	35.6	41.05/0.9737	38.72/0.9584	29.42/0.8795
CTVSR^[89]	16/14	34.5	41.20/0.9740	38.83/0.9580	29.28/0.8811
FTVSR++^[90]	—	10.8	−/−	−/−	28.80/0.8680
LGDFNet-BPP^[91]	—	9.0	40.81/0.9756	−/−	29.39/0.8798
RVRT^[93]	30/14	10.8	40.90/0.9729	38.59/0.9576	29.54/0.8810
DFVSR^[94]	—	7.1	40.97/0.9733	38.51/0.9571	29.56/0.8983
MFPI^[95]	−/−	7.3	41.08/0.9741	38.70/0.9579	29.34/0.8781

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表 4 真实场景下的VSR性能对比结果

Table 4 Performance comparison of real-world video super-resolution algorithm

对比方法	推理帧数	RealVSR	MVSR $ 4\times $
对比方法	推理帧数	PSNR (dB)/SSIM/LPIPS	PSNR (dB)/SSIM/LPIPS
RSDN^[103]	之前帧	23.91/0.7743/0.224	23.15/0.7533/0.279
FSTRN^[107]	7	23.36/0.7683/0.240	22.66/0.7433/0.315
TOF^[12]	7	23.62/0.7739/0.220	22.80/0.7502/0.279
TDAN^[51]	7	23.71/0.7737/0.229	23.07/0.7492/0.282
EDVR^[67]	7	23.96/0.7781/0.216	23.51/0.7611/0.268
BasicVSR^[75]	所有帧	24.00/0.7801/0.209	23.38/0.7594/0.270
MANA^[108]	所有帧	23.89/0.7781/0.224	23.15/0.7513/0.285
TTVSR^[87]	所有帧	24.08/0.7837/0.213	23.60/0.7686/0.277
ETDM^[82]	所有帧	24.13/0.7896/0.206	23.61/0.7662/0.260
BasicVSR++^[83]	所有帧	24.24/0.7933/0.216	23.70/0.7713/0.263
RealBasicVSR^[28]	所有帧	23.74/0.7676/0.174	23.15/0.7603/0.202
EAVSR^[30]	所有帧	24.20/0.7862/0.208	23.61/0.7618/0.264
EAVSR+^[30]	所有帧	24.41/0.7953/0.212	23.94/0.7726/0.259
EAVSRGAN+^[30]	所有帧	23.99/0.7726/0.170	23.35/0.7611/0.199

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表 5 不同帧间对齐方式的性能和参数比较

Table 5 Performance and parameter comparisons of different inter-frame alignment

对齐方式	参数量(M)	插值方法	光流(%)
对齐方式	参数量(M)	插值方法	GT	SpyNet
显式对齐(光流)	1.35	最近邻插值	31.84	31.78
		双线性插值	31.92	31.85
		双三次插值	31.93	31.89
混合对齐(光流引导	1.60	双线性插值	32.08	31.98
的可变形卷积)	1.60	双线性插值	32.08	31.98
混合对齐(光流引导	1.56	双线性插值	32.03	31.94
的可变形注意力)	1.56	双线性插值	32.03	31.94
混合对齐(光流引导	1.35	最近邻插值	31.81	31.82
的图像块对齐)	1.35	最近邻插值	31.81	31.82
混合对齐(光流引导	1.36	基于注意力的	32.14	32.05
的隐式对齐)	1.36	隐式插值	32.14	32.05

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表 6 GeForce RTX 3090平台下VSR的性能(PSNR/SSSIM)/推理时间对比结果

Table 6 Performance (PSNR/SSSIM) and inference time comparisons of VSR algorithm on GeForce RTX 3090 platform

对比方法	参数量 (M)	推理时间 (ms)	对齐方式	双三次插值下采样			高斯模糊下采样
对比方法	参数量 (M)	推理时间 (ms)	对齐方式	REDS (RGB通道)	Vimeo-90K-T (Y通道)	Vid4 (Y通道)	Vimeo-90K-T (Y通道)	Vid4 (Y通道)	UDM10 (Y通道)
Bicubic	—	<1	—	26.23/0.7319	31.32/0.8684	23.78/0.6374	31.30/0.8687	21.80/0.5346	28.47/0.8253
TOFlow^[12]	1.41	250	显式	27.96/0.7981	33.08/0.9054	25.89/0.7651	34.62/0.9212	25.85/0.7659	36.26/0.9438
DUF^[56]	5.8	737.5	无需	28.63/0.8251	−/−	27.33/0.8319	36.87/0.9447	27.38/0.8329	38.48/0.9605
EDVR^[67]	20.6	188.2	隐式	31.09/0.8800	37.61/0.9489	27.35/0.8264	37.81/0.9523	27.85/0.8503	39.89/0.9686
TMP^[61]	3.1	31.5	隐式	30.67/0.8710	−/−	27.10/0.8167	37.33/0.9481	27.61/0.8428	−/−
BasicVSR^[75]	6.3	45.4	显式	31.42/0.8909	37.18/0.9450	27.24/0.8251	37.53/0.9498	27.96/0.8553	39.96/0.9694
ICONVSR^[75]	8.7	58.4	显式	31.67/0.8948	37.47/0.9476	27.39/0.8279	37.84/0.9524	28.04/0.8570	40.03/0.9694
TTVSR^[87]	6.8	123.3	混合	32.12/0.9021	−/−	−/−	37.92/0.9526	28.40/0.8643	40.41/0.9712
VRT^[88]	35.6	1679	混合	32.17/0.9002	38.20/0.9530	27.93/0.8425	38.72/0.9584	29.37/0.8792	41.04/0.9737
BasicVSR++^[83]	7.3	60.2	混合	32.39/0.9069	37.79/0.9500	27.79/0.8400	38.21/0.9550	29.04/0.8753	40.72/0.9722
PSRT^[72]	13.4	1280.2	混合	32.72/0.9106	38.27/0.9536	28.07/0.8485	−/−	−/−	−/−
MIA-VSR^[97]	16.5	1194.6	无需	32.78 0.9220	38.22/0.9532	28.20/0.8507	−/−	−/−	−/−

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