# Fast Spatio-Temporal Residual Network for Video Super-Resolution

arXiv: https://arxiv.org/abs/1904.02870 · HTML: https://mlsanity.com/paper/1904.02870/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 30 of 34)

- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [Rethinking Alignment in Video Super-Resolution Transformers](https://mlsanity.com/paper/2207.08494/) (2022)
- [Learning Trajectory-Aware Transformer for Video Super-Resolution](https://mlsanity.com/paper/2204.04216/) (2022)
- [Language Repository for Long Video Understanding](https://mlsanity.com/paper/2403.14622/) (2024)
- [Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution](https://mlsanity.com/paper/2303.13767/) (2023)
- [FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring](https://mlsanity.com/paper/2401.03707/) (2024)
- [Efficient Video Compression via Content-Adaptive Super-Resolution](https://mlsanity.com/paper/2104.02322/) (2021)
- [E-CIR: Event-Enhanced Continuous Intensity Recovery](https://mlsanity.com/paper/2203.01935/) (2022)
- [Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution](https://mlsanity.com/paper/2208.03012/) (2022)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Reference-based Video Super-Resolution Using Multi-Camera Video Triplets](https://mlsanity.com/paper/2203.14537/) (2022)
- [Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling](https://mlsanity.com/paper/2204.07114/) (2022)
- [Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution](https://mlsanity.com/paper/2212.05342/) (2022)
- [FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution](https://mlsanity.com/paper/2105.05640/) (2021)
- [Asymmetric CNN for image super-resolution](https://mlsanity.com/paper/2103.13634/) (2021)
- [Structured Sparsity Learning for Efficient Video Super-Resolution](https://mlsanity.com/paper/2206.07687/) (2022)
- [Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution](https://mlsanity.com/paper/2305.05177/) (2023)
- [Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling](https://mlsanity.com/paper/2103.11744/) (2021)
- [Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask Upsampling](https://mlsanity.com/paper/2104.05778/) (2021)
- [Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution](https://mlsanity.com/paper/2201.01014/) (2022)
- [STDAN: Deformable Attention Network for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.06841/) (2022)
- [Out-of-boundary View Synthesis Towards Full-Frame Video Stabilization](https://mlsanity.com/paper/2108.09041/) (2021)
- [A Survey of Deep Learning Video Super-Resolution](https://mlsanity.com/paper/2506.03216/) (2025)
- [Progressive Deep Video Dehazing without Explicit Alignment Estimation](https://mlsanity.com/paper/2107.07837/) (2021)
- [Efficient Light Field Reconstruction via Spatio-Angular Dense Network](https://mlsanity.com/paper/2108.03635/) (2021)
- [PP-MSVSR: Multi-Stage Video Super-Resolution](https://mlsanity.com/paper/2112.02828/) (2021)
- [Writing in The Air: Unconstrained Text Recognition from Finger Movement Using Spatio-Temporal Convolution](https://mlsanity.com/paper/2104.09021/) (2021)
- [One-shot Transfer Learning for Population Mapping](https://mlsanity.com/paper/2108.06228/) (2021)
- [Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution](https://mlsanity.com/paper/2212.14046/) (2022)
- [DicFace: Dirichlet-Constrained Variational Codebook Learning for Temporally Coherent Video Face Restoration](https://mlsanity.com/paper/2506.13355/) (2025)

## Abstract (arXiv)

Recently, deep learning based video super-resolution (SR) methods have achieved promising performance. To simultaneously exploit the spatial and temporal information of videos, employing 3-dimensional (3D) convolutions is a natural approach. However, straight utilizing 3D convolutions may lead to an excessively high computational complexity which restricts the depth of video SR models and thus undermine the performance. In this paper, we present a novel fast spatio-temporal residual network (FSTRN) to adopt 3D convolutions for the video SR task in order to enhance the performance while maintaining a low computational load. Specifically, we propose a fast spatio-temporal residual block (FRB) that divide each 3D filter to the product of two 3D filters, which have considerably lower dimensions. Furthermore, we design a cross-space residual learning that directly links the low-resolution space and the high-resolution space, which can greatly relieve the computational burden on the feature fusion and up-scaling parts. Extensive evaluations and comparisons on benchmark datasets validate the strengths of the proposed approach and demonstrate that the proposed network significantly outperforms the current state-of-the-art methods.