# Model-blind Video Denoising Via Frame-to-frame Training

arXiv: https://arxiv.org/abs/1811.12766 · HTML: https://mlsanity.com/paper/1811.12766/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 30 of 34)

- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [Low-Light Image and Video Enhancement Using Deep Learning: A Survey](https://mlsanity.com/paper/2104.10729/) (2021)
- [Dancing under the stars: video denoising in starlight](https://mlsanity.com/paper/2204.04210/) (2022)
- [Patch Craft: Video Denoising by Deep Modeling and Patch Matching](https://mlsanity.com/paper/2103.13767/) (2021)
- [Optimal Transport for Unsupervised Denoising Learning](https://mlsanity.com/paper/2108.02574/) (2021)
- [Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images](https://mlsanity.com/paper/2101.09870/) (2021)
- [A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift](https://mlsanity.com/paper/2203.09294/) (2022)
- [Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution](https://mlsanity.com/paper/2207.01797/) (2022)
- [Deformation-Compensated Learning for Image Reconstruction without Ground Truth](https://mlsanity.com/paper/2107.05533/) (2021)
- [Self-Supervised Super-Resolution for Multi-Exposure Push-Frame Satellites](https://mlsanity.com/paper/2205.02031/) (2022)
- [RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset](https://mlsanity.com/paper/2305.00767/) (2023)
- [Recurrent Self-Supervised Video Denoising with Denser Receptive Field](https://mlsanity.com/paper/2308.03608/) (2023)
- [PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples](https://mlsanity.com/paper/2210.04866/) (2022)
- [Self-Supervised Learning from Noisy and Incomplete Data](https://mlsanity.com/paper/2601.03244/) (2026)
- [Spatiotemporal Blind-Spot Network with Calibrated Flow Alignment for Self-Supervised Video Denoising](https://mlsanity.com/paper/2412.11820/) (2024)
- [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)
- [L1BSR: Exploiting Detector Overlap for Self-Supervised Single-Image Super-Resolution of Sentinel-2 L1B Imagery](https://mlsanity.com/paper/2304.06871/) (2023)
- [Unsupervised Coordinate-Based Video Denoising](https://mlsanity.com/paper/2307.00179/) (2023)
- [A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2101.07613/) (2021)
- [Multi-task Video Enhancement for Dental Interventions](https://mlsanity.com/paper/2210.16236/) (2022)
- [Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning](https://mlsanity.com/paper/2305.00691/) (2023)
- [Unsupervised Microscopy Video Denoising](https://mlsanity.com/paper/2404.12163/) (2024)
- [Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable](https://mlsanity.com/paper/2504.03136/) (2025)
- [Stabilize, Decompose, and Denoise: Self-Supervised Fluoroscopy Denoising](https://mlsanity.com/paper/2208.14022/) (2022)
- [Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques](https://mlsanity.com/paper/2507.08375/) (2025)
- [DMAligner: Enhancing Image Alignment via Diffusion Model Based View Synthesis](https://mlsanity.com/paper/2602.23022/) (2026)
- [Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool](https://mlsanity.com/paper/2103.16115/) (2021)
- [Subspace modeling for fast and high-sensitivity X-ray chemical imaging](https://mlsanity.com/paper/2201.00259/) (2022)
- [Towards Real-World Video Denosing: A Practical Video Denosing Dataset and Network](https://mlsanity.com/paper/2207.01356/) (2022)

## Abstract (arXiv)

Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved by fine-tuning a pre-trained AWGN denoising network to the video with a novel frame-to-frame training strategy. Our denoiser can be used without knowledge of the origin of the video or burst and the post processing steps applied from the camera sensor. The on-line process only requires a couple of frames before achieving visually-pleasing results for a wide range of perturbations. It nonetheless reaches state of the art performance for standard Gaussian noise, and can be used off-line with still better performance.