SOTAVerified

Video Super-Resolution

Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.

( Image credit: Detail-revealing Deep Video Super-Resolution )

Papers

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Showing 1–10 of 281 papers

TitleStatusHype
Compressed Video Super-Resolution based on Hierarchical Encoding—0
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-ResolutionCode0
ICME 2025 Grand Challenge on Video Super-Resolution for Video ConferencingCode1
LiftVSR: Lifting Image Diffusion to Video Super-Resolution via Hybrid Temporal Modeling with Only 4RTX 4090s—0
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-ResolutionCode0
DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation—0
Omnidirectional Video Super-Resolution using Deep Learning—0
A Survey of Deep Learning Video Super-Resolution—0
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space—0
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-ResolutionCode2
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All datasetsMSU Super-Resolution for Video CompressionMSU Video Upscalers: Quality EnhancementMSU Video Super Resolution Benchmark: Detail RestorationVid4 - 4x upscalingVid4 - 4x upscaling - BD degradationREDS4- 4x upscalingUDM10 - 4x upscalingUltra Video Group HD - 4x upscalingFalling ObjectsTbDTbD-3DVimeo90K

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RAMS (ours)Average PSNR48.23—Unverified
2DeepSUM[41]Average PSNR47.84—Unverified
3ESPCNAverage PSNR37.91—Unverified
4SRCNNAverage PSNR37.52—Unverified
5bicubicAverage PSNR36.2—Unverified