# FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation

arXiv: https://arxiv.org/abs/1907.01361 · HTML: https://mlsanity.com/paper/1907.01361/

## Key facts

- Cited by 128 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 30 of 128)

- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution](https://mlsanity.com/paper/2205.03409/) (2022)
- [Recurrent Video Restoration Transformer with Guided Deformable Attention](https://mlsanity.com/paper/2206.02146/) (2022)
- [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --](https://mlsanity.com/paper/2301.03362/) (2023)
- [A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift](https://mlsanity.com/paper/2206.10810/) (2022)
- [Physics-based Noise Modeling for Extreme Low-light Photography](https://mlsanity.com/paper/2108.02158/) (2021)
- [Dancing under the stars: video denoising in starlight](https://mlsanity.com/paper/2204.04210/) (2022)
- [Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach](https://mlsanity.com/paper/2305.12726/) (2023)
- [RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.14186/) (2022)
- [Plug-and-Play Algorithms for Video Snapshot Compressive Imaging](https://mlsanity.com/paper/2101.04822/) (2021)
- [Patch Craft: Video Denoising by Deep Modeling and Patch Matching](https://mlsanity.com/paper/2103.13767/) (2021)
- [DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models](https://mlsanity.com/paper/2407.01519/) (2024)
- [Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging](https://mlsanity.com/paper/2109.06548/) (2021)
- [Bringing Old Films Back to Life](https://mlsanity.com/paper/2203.17276/) (2022)
- [Physics-Informed Computer Vision: A Review and Perspectives](https://mlsanity.com/paper/2305.18035/) (2023)
- [NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results](https://mlsanity.com/paper/2104.10781/) (2021)
- [Revisiting Temporal Alignment for Video Restoration](https://mlsanity.com/paper/2111.15288/) (2021)
- [Video Demoireing with Relation-Based Temporal Consistency](https://mlsanity.com/paper/2204.02957/) (2022)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [Skip-Attention: Improving Vision Transformers by Paying Less Attention](https://mlsanity.com/paper/2301.02240/) (2023)
- [Neural Compression-Based Feature Learning for Video Restoration](https://mlsanity.com/paper/2203.09208/) (2022)
- [Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior](https://mlsanity.com/paper/2303.09757/) (2023)
- [On the Generalization of BasicVSR++ to Video Deblurring and Denoising](https://mlsanity.com/paper/2204.05308/) (2022)
- [Deep Face Restoration: A Survey](https://mlsanity.com/paper/2211.02831/) (2022)
- [Binarized Low-light Raw Video Enhancement](https://mlsanity.com/paper/2403.19944/) (2024)
- [FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table](https://mlsanity.com/paper/2308.06749/) (2023)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network](https://mlsanity.com/paper/2205.04721/) (2022)
- [AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results](https://mlsanity.com/paper/2508.16830/) (2025)
- [HDR Video Reconstruction with Tri-Exposure Quad-Bayer Sensors](https://mlsanity.com/paper/2103.10982/) (2021)

## Abstract (arXiv)

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Until recently, video denoising with neural networks had been a largely under explored domain, and existing methods could not compete with the performance of the best patch-based methods. The approach we introduce in this paper, called FastDVDnet, shows similar or better performance than other state-of-the-art competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as fast runtimes, and the ability to handle a wide range of noise levels with a single network model. The characteristics of its architecture make it possible to avoid using a costly motion compensation stage while achieving excellent performance. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics.