# Space-Time-Aware Multi-Resolution Video Enhancement

arXiv: https://arxiv.org/abs/2003.13170 · HTML: https://mlsanity.com/paper/2003.13170/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 30 of 49)

- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [XVFI: eXtreme Video Frame Interpolation](https://mlsanity.com/paper/2103.16206/) (2021)
- [VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution](https://mlsanity.com/paper/2206.04647/) (2022)
- [VEnhancer: Generative Space-Time Enhancement for Video Generation](https://mlsanity.com/paper/2407.07667/) (2024)
- [Temporal Modulation Network for Controllable Space-Time Video Super-Resolution](https://mlsanity.com/paper/2104.10642/) (2021)
- [Video Event Restoration Based on Keyframes for Video Anomaly Detection](https://mlsanity.com/paper/2304.05112/) (2023)
- [RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.14186/) (2022)
- [SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation](https://mlsanity.com/paper/2104.10325/) (2021)
- [COMISR: Compression-Informed Video Super-Resolution](https://mlsanity.com/paper/2105.01237/) (2021)
- [Learning for Unconstrained Space-Time Video Super-Resolution](https://mlsanity.com/paper/2102.13011/) (2021)
- [DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting](https://mlsanity.com/paper/2111.09985/) (2021)
- [PMQ-VE: Progressive Multi-Frame Quantization for Video Enhancement](https://mlsanity.com/paper/2505.12266/) (2025)
- [NTIRE 2021 Challenge on Video Super-Resolution](https://mlsanity.com/paper/2104.14852/) (2021)
- [Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning](https://mlsanity.com/paper/2205.05264/) (2022)
- [NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results](https://mlsanity.com/paper/2505.03007/) (2025)
- [Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling](https://mlsanity.com/paper/2104.05778/) (2021)
- [BF-STVSR: B-Splines and Fourier-Best Friends for High Fidelity Spatial-Temporal Video Super-Resolution](https://mlsanity.com/paper/2501.11043/) (2025)
- [AceVFI: A Comprehensive Survey of Advances in Video Frame Interpolation](https://mlsanity.com/paper/2506.01061/) (2025)
- [Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution](https://mlsanity.com/paper/2110.06786/) (2021)
- [MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2110.15327/) (2021)
- [Video Frame Interpolation with Densely Queried Bilateral Correlation](https://mlsanity.com/paper/2304.13596/) (2023)
- [Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution](https://mlsanity.com/paper/2310.17294/) (2023)
- [Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images](https://mlsanity.com/paper/2302.12491/) (2023)
- [STDAN: Deformable Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.06841/) (2022)
- [NTIRE 2023 Quality Assessment of Video Enhancement Challenge](https://mlsanity.com/paper/2307.09729/) (2023)
- [HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera](https://mlsanity.com/paper/2405.13389/) (2024)
- [Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2104.07473/) (2021)
- [Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements](https://mlsanity.com/paper/2312.07835/) (2023)
- [Learning-Based Video Coding with Joint Deep Compression and Enhancement](https://mlsanity.com/paper/2111.14474/) (2021)
- [You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution](https://mlsanity.com/paper/2207.06345/) (2022)

## Abstract (arXiv)

We consider the problem of space-time super-resolution (ST-SR): increasing spatial resolution of video frames and simultaneously interpolating frames to increase the frame rate. Modern approaches handle these axes one at a time. In contrast, our proposed model called STARnet super-resolves jointly in space and time. This allows us to leverage mutually informative relationships between time and space: higher resolution can provide more detailed information about motion, and higher frame-rate can provide better pixel alignment. The components of our model that generate latent low- and high-resolution representations during ST-SR can be used to finetune a specialized mechanism for just spatial or just temporal super-resolution. Experimental results demonstrate that STARnet improves the performances of space-time, spatial, and temporal video super-resolution by substantial margins on publicly available datasets.