# Recurrent Back-Projection Network for Video Super-Resolution

arXiv: https://arxiv.org/abs/1903.10128 · HTML: https://mlsanity.com/paper/1903.10128/

## Key facts

- Cited by 148 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 30 of 148)

- [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)
- [Video Frame Interpolation Transformer](https://mlsanity.com/paper/2111.13817/) (2021)
- [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021)
- [Generating Long Videos of Dynamic Scenes](https://mlsanity.com/paper/2206.03429/) (2022)
- [Real-World Single Image Super-Resolution: A Brief Review](https://mlsanity.com/paper/2103.02368/) (2021)
- [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)
- [Temporal Modulation Network for Controllable Space-Time Video Super-Resolution](https://mlsanity.com/paper/2104.10642/) (2021)
- [RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.14186/) (2022)
- [Deep Reparametrization of Multi-Frame Super-Resolution and Denoising](https://mlsanity.com/paper/2108.08286/) (2021)
- [Enhancing Video Super-Resolution via Implicit Resampling-based Alignment](https://mlsanity.com/paper/2305.00163/) (2023)
- [Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution](https://mlsanity.com/paper/2303.13767/) (2023)
- [Bringing Old Films Back to Life](https://mlsanity.com/paper/2203.17276/) (2022)
- [Omniscient Video Super-Resolution](https://mlsanity.com/paper/2103.15683/) (2021)
- [VideoGigaGAN: Towards Detail-rich Video Super-Resolution](https://mlsanity.com/paper/2404.12388/) (2024)
- [Mitigating Artifacts in Real-World Video Super-Resolution Models](https://mlsanity.com/paper/2212.07339/) (2022)
- [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)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results](https://mlsanity.com/paper/2104.10781/) (2021)
- [COMISR: Compression-Informed Video Super-Resolution](https://mlsanity.com/paper/2105.01237/) (2021)
- [ClothFormer:Taming Video Virtual Try-on in All Module](https://mlsanity.com/paper/2204.12151/) (2022)
- [Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution](https://mlsanity.com/paper/2208.03012/) (2022)
- [Revisiting Temporal Alignment for Video Restoration](https://mlsanity.com/paper/2111.15288/) (2021)
- [A Comprehensive Review of Deep Learning-based Single Image Super-resolution](https://mlsanity.com/paper/2102.09351/) (2021)
- [Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution](https://mlsanity.com/paper/2403.17000/) (2024)
- [NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results](https://mlsanity.com/paper/2106.03839/) (2021)
- [Can SAM Boost Video Super-Resolution?](https://mlsanity.com/paper/2305.06524/) (2023)

## Abstract (arXiv)

We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super-resolution path for the target frame. In contrast to most prior work where frames are pooled together by stacking or warping, our model, the Recurrent Back-Projection Network (RBPN) treats each context frame as a separate source of information. These sources are combined in an iterative refinement framework inspired by the idea of back-projection in multiple-image super-resolution. This is aided by explicitly representing estimated inter-frame motion with respect to the target, rather than explicitly aligning frames. We propose a new video super-resolution benchmark, allowing evaluation at a larger scale and considering videos in different motion regimes. Experimental results demonstrate that our RBPN is superior to existing methods on several datasets.