# On the Generalization of BasicVSR++ to Video Deblurring and Denoising

arXiv: https://arxiv.org/abs/2204.05308 · HTML: https://mlsanity.com/paper/2204.05308/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](https://mlsanity.com/paper/1905.02716/) (2019)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment](https://mlsanity.com/paper/2104.13371/) (2021)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond](https://mlsanity.com/paper/2012.02181/) (2020)
- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [Video Object Segmentation with Language Referring Expressions](https://mlsanity.com/paper/1803.08006/) (2018)
- [Recurrent Back-Projection Network for Video Super-Resolution](https://mlsanity.com/paper/1903.10128/) (2019)
- [FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation](https://mlsanity.com/paper/1907.01361/) (2019)
- [Video Super-Resolution Transformer](https://mlsanity.com/paper/2106.06847/) (2021)
- [Spatio-Temporal Filter Adaptive Network for Video Deblurring](https://mlsanity.com/paper/1904.12257/) (2019)
- [Cascaded Deep Video Deblurring Using Temporal Sharpness Prior](https://mlsanity.com/paper/2004.02501/) (2020)
- [Understanding Deformable Alignment in Video Super-Resolution](https://mlsanity.com/paper/2009.07265/) (2020)
- [Adversarial Spatio-Temporal Learning for Video Deblurring](https://mlsanity.com/paper/1804.00533/) (2018)
- [DVDnet: A Fast Network for Deep Video Denoising](https://mlsanity.com/paper/1906.11890/) (2019)
- [ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring](https://mlsanity.com/paper/2103.04260/) (2021)
- [Flow-Guided Sparse Transformer for Video Deblurring](https://mlsanity.com/paper/2201.01893/) (2022)
- [Generalized Video Deblurring for Dynamic Scenes](https://mlsanity.com/paper/1507.02438/) (2015)
- [Patch Craft: Video Denoising by Deep Modeling and Patch Matching](https://mlsanity.com/paper/2103.13767/) (2021)
- [Learning Blind Motion Deblurring](https://mlsanity.com/paper/1708.04208/) (2017)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results](https://mlsanity.com/paper/2104.10781/) (2021)
- [Neural Compression-Based Feature Learning for Video Restoration](https://mlsanity.com/paper/2203.09208/) (2022)
- [NTIRE 2021 Challenge on Video Super-Resolution](https://mlsanity.com/paper/2104.14852/) (2021)
- [Non-Local Video Denoising by CNN](https://mlsanity.com/paper/1811.12758/) (2018)

## Built on by (most cited first, 19 of 19)

- [ProPainter: Improving Propagation and Transformer for Video Inpainting](https://mlsanity.com/paper/2309.03897/) (2023)
- [A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift](https://mlsanity.com/paper/2206.10810/) (2022)
- [Imaging through the Atmosphere using Turbulence Mitigation Transformer](https://mlsanity.com/paper/2207.06465/) (2022)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [Multi-task Image Restoration Guided By Robust DINO Features](https://mlsanity.com/paper/2312.01677/) (2023)
- [SMC++: Masked Learning of Unsupervised Video Semantic Compression](https://mlsanity.com/paper/2406.04765/) (2024)
- [MoA-VR: A Mixture-of-Agents System Towards All-in-One Video Restoration](https://mlsanity.com/paper/2510.08508/) (2025)
- [Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model](https://mlsanity.com/paper/2408.13459/) (2024)
- [Reference-based Image and Video Super-Resolution via C2-Matching](https://mlsanity.com/paper/2212.09581/) (2022)
- [Recurrent Self-Supervised Video Denoising with Denser Receptive Field](https://mlsanity.com/paper/2308.03608/) (2023)
- [Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising](https://mlsanity.com/paper/2208.11803/) (2022)
- [Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?](https://mlsanity.com/paper/2405.20392/) (2024)
- [Towards Blind Bitstream-corrupted Video Recovery via a Visual Foundation Model-driven Framework](https://mlsanity.com/paper/2507.22481/) (2025)
- [Low-Resource Video Super-Resolution using Memory, Wavelets, and Deformable Convolutions](https://mlsanity.com/paper/2502.01816/) (2025)
- [VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising](https://mlsanity.com/paper/2401.14754/) (2024)
- [Joint Reference Frame Synthesis and Post Filter Enhancement for Versatile Video Coding](https://mlsanity.com/paper/2404.18058/) (2024)
- [Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration](https://mlsanity.com/paper/2303.16411/) (2023)
- [Adapting MIMO video restoration networks to low latency constraints](https://mlsanity.com/paper/2408.12439/) (2024)
- [Video Denoising in Fluorescence Guided Surgery](https://mlsanity.com/paper/2411.09798/) (2024)

## Abstract (arXiv)

The exploitation of long-term information has been a long-standing problem in video restoration. The recent BasicVSR and BasicVSR++ have shown remarkable performance in video super-resolution through long-term propagation and effective alignment. Their success has led to a question of whether they can be transferred to different video restoration tasks. In this work, we extend BasicVSR++ to a generic framework for video restoration tasks. In tasks where inputs and outputs possess identical spatial size, the input resolution is reduced by strided convolutions to maintain efficiency. With only minimal changes from BasicVSR++, the proposed framework achieves compelling performance with great efficiency in various video restoration tasks including video deblurring and denoising. Notably, BasicVSR++ achieves comparable performance to Transformer-based approaches with up to 79% of parameter reduction and 44x speedup. The promising results demonstrate the importance of propagation and alignment in video restoration tasks beyond just video super-resolution. Code and models are available at https://github.com/ckkelvinchan/BasicVSR_PlusPlus.