# Noise2NoiseFlow: Realistic Camera Noise Modeling without Clean Images

arXiv: https://arxiv.org/abs/2206.01103 · HTML: https://mlsanity.com/paper/2206.01103/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 45 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Noise2Noise: Learning Image Restoration without Clean Data](https://mlsanity.com/paper/1803.04189/) (2018)
- [Noise2Void - Learning Denoising from Single Noisy Images](https://mlsanity.com/paper/1811.10980/) (2018)
- [Noise2Self: Blind Denoising by Self-Supervision](https://mlsanity.com/paper/1901.11365/) (2019)
- [Benchmarking Denoising Algorithms with Real Photographs](https://mlsanity.com/paper/1707.01313/) (2017)
- [Noisier2Noise: Learning to Denoise from Unpaired Noisy Data](https://mlsanity.com/paper/1910.11908/) (2019)
- [Noise Flow: Noise Modeling with Conditional Normalizing Flows](https://mlsanity.com/paper/1908.08453/) (2019)
- [Real-world Noisy Image Denoising: A New Benchmark](https://mlsanity.com/paper/1804.02603/) (2018)
- [Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks](https://mlsanity.com/paper/2001.05992/) (2020)
- [Rethinking Noise Synthesis and Modeling in Raw Denoising](https://mlsanity.com/paper/2110.04756/) (2021)
- [C2N: Practical Generative Noise Modeling for Real-World Denoising](https://mlsanity.com/paper/2202.09533/) (2022)
- [When AWGN-based Denoiser Meets Real Noises](https://mlsanity.com/paper/1904.03485/) (2019)
- [RENOIR - A Dataset for Real Low-Light Image Noise Reduction](https://mlsanity.com/paper/1409.8230/) (2014)
- [GRDN:Grouped Residual Dense Network for Real Image Denoising and GAN-based Real-world Noise Modeling](https://mlsanity.com/paper/1905.11172/) (2019)
- [DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows](https://mlsanity.com/paper/2101.05796/) (2021)
- [Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network](https://mlsanity.com/paper/2105.04746/) (2021)

## Built on by (most cited first, 17 of 17)

- [Stimulating Diffusion Model for Image Denoising via Adaptive Embedding and Ensembling](https://mlsanity.com/paper/2307.03992/) (2023)
- [Self-supervised Image Denoising with Downsampled Invariance Loss and Conditional Blind-Spot Network](https://mlsanity.com/paper/2304.09507/) (2023)
- [LED: A Large-scale Real-world Paired Dataset for Event Camera Denoising](https://mlsanity.com/paper/2405.19718/) (2024)
- [BSRAW: Improving Blind RAW Image Super-Resolution](https://mlsanity.com/paper/2312.15487/) (2023)
- [Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising](https://mlsanity.com/paper/2208.11803/) (2022)
- [Patch-Craft Self-Supervised Training for Correlated Image Denoising](https://mlsanity.com/paper/2211.09919/) (2022)
- [A Generative Model for Digital Camera Noise Synthesis](https://mlsanity.com/paper/2303.09199/) (2023)
- [Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer](https://mlsanity.com/paper/2407.09094/) (2024)
- [Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model](https://mlsanity.com/paper/2308.03448/) (2023)
- [Modeling Dual-Exposure Quad-Bayer Patterns for Joint Denoising and Deblurring](https://mlsanity.com/paper/2412.07256/) (2024)
- [Towards Controllable Real Image Denoising with Camera Parameters](https://mlsanity.com/paper/2507.01587/) (2025)
- [Blind Augmentation: Calibration-free Camera Distortion Model Estimation for Real-time Mixed-reality Consistency](https://mlsanity.com/paper/2503.01387/) (2025)
- [NM-FlowGAN: Modeling sRGB Noise without Paired Images using a Hybrid Approach of Normalizing Flows and GAN](https://mlsanity.com/paper/2312.10112/) (2023)
- [Adaptive Domain Learning for Cross-domain Image Denoising](https://mlsanity.com/paper/2411.01472/) (2024)
- [Hybrid Event Frame Sensors: Modeling, Calibration, and Simulation](https://mlsanity.com/paper/2511.18037/) (2025)
- [TM-BSN: Triangular-Masked Blind-Spot Network for Real-World Self-Supervised Image Denoising](https://mlsanity.com/paper/2604.04484/) (2026)
- [Poisson2Gaussian: Noise Gaussianization to Enhance Image Denoising](https://mlsanity.com/paper/2606.23098/) (2026)

## Abstract (arXiv)

Image noise modeling is a long-standing problem with many applications in computer vision. Early attempts that propose simple models, such as signal-independent additive white Gaussian noise or the heteroscedastic Gaussian noise model (a.k.a., camera noise level function) are not sufficient to learn the complex behavior of the camera sensor noise. Recently, more complex learning-based models have been proposed that yield better results in noise synthesis and downstream tasks, such as denoising. However, their dependence on supervised data (i.e., paired clean images) is a limiting factor given the challenges in producing ground-truth images. This paper proposes a framework for training a noise model and a denoiser simultaneously while relying only on pairs of noisy images rather than noisy/clean paired image data. We apply this framework to the training of the Noise Flow architecture. The noise synthesis and density estimation results show that our framework outperforms previous signal-processing-based noise models and is on par with its supervised counterpart. The trained denoiser is also shown to significantly improve upon both supervised and weakly supervised baseline denoising approaches. The results indicate that the joint training of a denoiser and a noise model yields significant improvements in the denoiser.