# ViDeNN: Deep Blind Video Denoising

arXiv: https://arxiv.org/abs/1904.10898 · HTML: https://mlsanity.com/paper/1904.10898/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 30 of 40)

- [Dancing under the stars: video denoising in starlight](https://mlsanity.com/paper/2204.04210/) (2022)
- [RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2203.14186/) (2022)
- [Patch Craft: Video Denoising by Deep Modeling and Patch Matching](https://mlsanity.com/paper/2103.13767/) (2021)
- [Bringing Old Films Back to Life](https://mlsanity.com/paper/2203.17276/) (2022)
- [Blind Video Deflickering by Neural Filtering with a Flawed Atlas](https://mlsanity.com/paper/2303.08120/) (2023)
- [Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones](https://mlsanity.com/paper/2204.05532/) (2022)
- [AIM 2025 Low-light RAW Video Denoising Challenge: Dataset, Methods and Results](https://mlsanity.com/paper/2508.16830/) (2025)
- [Contextual colorization and denoising for low-light ultra high resolution sequences](https://mlsanity.com/paper/2101.01597/) (2021)
- [Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution](https://mlsanity.com/paper/2207.01797/) (2022)
- [Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising](https://mlsanity.com/paper/2101.10760/) (2021)
- [Recurrent Self-Supervised Video Denoising with Denser Receptive Field](https://mlsanity.com/paper/2308.03608/) (2023)
- [A Comparison of Image Denoising Methods](https://mlsanity.com/paper/2304.08990/) (2023)
- [Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising](https://mlsanity.com/paper/2208.11803/) (2022)
- [Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoising](https://mlsanity.com/paper/2604.11468/) (2026)
- [Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient Mask](https://mlsanity.com/paper/2103.02861/) (2021)
- [Frequency Domain Loss Function for Deep Exposure Correction of Dark Images](https://mlsanity.com/paper/2104.10856/) (2021)
- [RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression](https://mlsanity.com/paper/2203.10183/) (2022)
- [Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?](https://mlsanity.com/paper/2204.11493/) (2022)
- [Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel Transformer](https://mlsanity.com/paper/2205.00214/) (2022)
- [Unsupervised Coordinate-Based Video Denoising](https://mlsanity.com/paper/2307.00179/) (2023)
- [BONES: Near-Optimal Neural-Enhanced Video Streaming](https://mlsanity.com/paper/2310.09920/) (2023)
- [Quanta Video Restoration](https://mlsanity.com/paper/2410.14994/) (2024)
- [A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2101.07613/) (2021)
- [Fast and Accurate: Video Enhancement using Sparse Depth](https://mlsanity.com/paper/2103.08764/) (2021)
- [VJT: A Video Transformer on Joint Tasks of Deblurring, Low-light Enhancement and Denoising](https://mlsanity.com/paper/2401.14754/) (2024)
- [Unsupervised Microscopy Video Denoising](https://mlsanity.com/paper/2404.12163/) (2024)
- [Lightweight Video Denoising Using a Classic Bayesian Backbone](https://mlsanity.com/paper/2408.03904/) (2024)
- [Joint Flow And Feature Refinement Using Attention For Video Restoration](https://mlsanity.com/paper/2505.16434/) (2025)
- [Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising](https://mlsanity.com/paper/2208.14022/) (2022)
- [Color Image Denoising Using The Green Channel Prior](https://mlsanity.com/paper/2402.08235/) (2024)

## Abstract (arXiv)

We propose ViDeNN: a CNN for Video Denoising without prior knowledge on the noise distribution (blind denoising). The CNN architecture uses a combination of spatial and temporal filtering, learning to spatially denoise the frames first and at the same time how to combine their temporal information, handling objects motion, brightness changes, low-light conditions and temporal inconsistencies. We demonstrate the importance of the data used for CNNs training, creating for this purpose a specific dataset for low-light conditions. We test ViDeNN on common benchmarks and on self-collected data, achieving good results comparable with the state-of-the-art.