# Efficient Video Super-Resolution through Recurrent Latent Space Propagation

arXiv: https://arxiv.org/abs/1909.08080 · HTML: https://mlsanity.com/paper/1909.08080/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 30 of 69)

- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [Recurrent Video Restoration Transformer with Guided Deformable Attention](https://mlsanity.com/paper/2206.02146/) (2022)
- [A Comprehensive Survey on Segment Anything Model for Vision and Beyond](https://mlsanity.com/paper/2305.08196/) (2023)
- [Learning Trajectory-Aware Transformer for Video Super-Resolution](https://mlsanity.com/paper/2204.04216/) (2022)
- [STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution](https://mlsanity.com/paper/2501.02976/) (2025)
- [FlashVSR: Towards Real-Time Diffusion-Based Streaming Video Super-Resolution](https://mlsanity.com/paper/2510.12747/) (2025)
- [Enhancing Video Super-Resolution via Implicit Resampling-based Alignment](https://mlsanity.com/paper/2305.00163/) (2023)
- [RealViformer: Investigating Attention for Real-World Video Super-Resolution](https://mlsanity.com/paper/2407.13987/) (2024)
- [Omniscient Video Super-Resolution](https://mlsanity.com/paper/2103.15683/) (2021)
- [Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention](https://mlsanity.com/paper/2401.06312/) (2024)
- [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)
- [COMISR: Compression-Informed Video Super-Resolution](https://mlsanity.com/paper/2105.01237/) (2021)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos](https://mlsanity.com/paper/2206.07038/) (2022)
- [Can SAM Boost Video Super-Resolution?](https://mlsanity.com/paper/2305.06524/) (2023)
- [Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling](https://mlsanity.com/paper/2204.07114/) (2022)
- [Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report](https://mlsanity.com/paper/2105.08826/) (2021)
- [Self-Conditioned Probabilistic Learning of Video Rescaling](https://mlsanity.com/paper/2107.11639/) (2021)
- [Precipitation Downscaling with Spatiotemporal Video Diffusion](https://mlsanity.com/paper/2312.06071/) (2023)
- [Temporal Consistency Learning of inter-frames for Video Super-Resolution](https://mlsanity.com/paper/2211.01639/) (2022)
- [One-Step Diffusion for Detail-Rich and Temporally Consistent Video Super-Resolution](https://mlsanity.com/paper/2506.15591/) (2025)
- [Structured Sparsity Learning for Efficient Video Super-Resolution](https://mlsanity.com/paper/2206.07687/) (2022)
- [UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space](https://mlsanity.com/paper/2505.19958/) (2025)
- [Learning Truncated Causal History Model for Video Restoration](https://mlsanity.com/paper/2410.03936/) (2024)
- [Online Video Super-Resolution with Convolutional Kernel Bypass Graft](https://mlsanity.com/paper/2208.02470/) (2022)
- [Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution](https://mlsanity.com/paper/2310.17294/) (2023)
- [TMP: Temporal Motion Propagation for Online Video Super-Resolution](https://mlsanity.com/paper/2312.09909/) (2023)
- [Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion](https://mlsanity.com/paper/2512.23709/) (2025)
- [Stable Long-Term Recurrent Video Super-Resolution](https://mlsanity.com/paper/2112.08950/) (2021)

## Abstract (arXiv)

With the recent trend for ultra high definition displays, the demand for high quality and efficient video super-resolution (VSR) has become more important than ever. Previous methods adopt complex motion compensation strategies to exploit temporal information when estimating the missing high frequency details. However, as the motion estimation problem is a highly challenging problem, inaccurate motion compensation may affect the performance of VSR algorithms. Furthermore, the complex motion compensation module may also introduce a heavy computational burden, which limits the application of these methods in real systems. In this paper, we propose an efficient recurrent latent space propagation (RLSP) algorithm for fast VSR. RLSP introduces high-dimensional latent states to propagate temporal information between frames in an implicit manner. Our experimental results show that RLSP is a highly efficient and effective method to deal with the VSR problem. We outperform current state-of-the-art method DUF with over 70x speed-up.