# Estimating Fine-Grained Noise Model via Contrastive Learning

arXiv: https://arxiv.org/abs/2204.01716 · HTML: https://mlsanity.com/paper/2204.01716/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 52 references that are in the map.
- First posted on arXiv 3 Apr 2022.

## Builds on (most cited first, 25 of 25)

- [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)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Learning to See in the Dark](https://mlsanity.com/paper/1805.01934/) (2018)
- [Toward Convolutional Blind Denoising of Real Photographs](https://mlsanity.com/paper/1807.04686/) (2018)
- [Unprocessing Images for Learned Raw Denoising](https://mlsanity.com/paper/1811.11127/) (2018)
- [MemNet: A Persistent Memory Network for Image Restoration](https://mlsanity.com/paper/1708.02209/) (2017)
- [Benchmarking Denoising Algorithms with Real Photographs](https://mlsanity.com/paper/1707.01313/) (2017)
- [Noise Flow: Noise Modeling with Conditional Normalizing Flows](https://mlsanity.com/paper/1908.08453/) (2019)
- [Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation](https://mlsanity.com/paper/2007.05946/) (2020)
- [Supervised Raw Video Denoising with a Benchmark Dataset on Dynamic Scenes](https://mlsanity.com/paper/2003.14013/) (2020)
- [Practical Deep Raw Image Denoising on Mobile Devices](https://mlsanity.com/paper/2010.06935/) (2020)
- [Unpaired Learning of Deep Image Denoising](https://mlsanity.com/paper/2008.13711/) (2020)
- [Physics-based Noise Modeling for Extreme Low-light Photography](https://mlsanity.com/paper/2108.02158/) (2021)
- [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)
- [Learning Camera-Aware Noise Models](https://mlsanity.com/paper/2008.09370/) (2020)
- [FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise](https://mlsanity.com/paper/2105.10967/) (2021)
- [Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser](https://mlsanity.com/paper/2103.10234/) (2021)

## Built on by (most cited first, 14 of 14)

- [RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image](https://mlsanity.com/paper/2309.02020/) (2023)
- [Binarized Low-light Raw Video Enhancement](https://mlsanity.com/paper/2403.19944/) (2024)
- [ODCR: Orthogonal Decoupling Contrastive Regularization for Unpaired Image Dehazing](https://mlsanity.com/paper/2404.17825/) (2024)
- [EventHDR: from Event to High-Speed HDR Videos and Beyond](https://mlsanity.com/paper/2409.17029/) (2024)
- [Multi-Object Tracking in the Dark](https://mlsanity.com/paper/2405.06600/) (2024)
- [When Semantic Segmentation Meets Frequency Aliasing](https://mlsanity.com/paper/2403.09065/) (2024)
- [Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement](https://mlsanity.com/paper/2409.07040/) (2024)
- [Recurrent Spike-based Image Restoration under General Illumination](https://mlsanity.com/paper/2308.03018/) (2023)
- [YOND: Practical Blind Raw Image Denoising Free from Camera-Specific Data Dependency](https://mlsanity.com/paper/2506.03645/) (2025)
- [A Generative Model for Digital Camera Noise Synthesis](https://mlsanity.com/paper/2303.09199/) (2023)
- [Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model](https://mlsanity.com/paper/2308.03448/) (2023)
- [Towards a Universal Image Degradation Model via Content-Degradation Disentanglement](https://mlsanity.com/paper/2505.12860/) (2025)
- [Technique Report of CVPR 2024 PBDL Challenges](https://mlsanity.com/paper/2406.10744/) (2024)
- [Adaptive Domain Learning for Cross-domain Image Denoising](https://mlsanity.com/paper/2411.01472/) (2024)

## Abstract (arXiv)

Image denoising has achieved unprecedented progress as great efforts have been made to exploit effective deep denoisers. To improve the denoising performance in realworld, two typical solutions are used in recent trends: devising better noise models for the synthesis of more realistic training data, and estimating noise level function to guide non-blind denoisers. In this work, we combine both noise modeling and estimation, and propose an innovative noise model estimation and noise synthesis pipeline for realistic noisy image generation. Specifically, our model learns a noise estimation model with fine-grained statistical noise model in a contrastive manner. Then, we use the estimated noise parameters to model camera-specific noise distribution, and synthesize realistic noisy training data. The most striking thing for our work is that by calibrating noise models of several sensors, our model can be extended to predict other cameras. In other words, we can estimate cameraspecific noise models for unknown sensors with only testing images, without laborious calibration frames or paired noisy/clean data. The proposed pipeline endows deep denoisers with competitive performances with state-of-the-art real noise modeling methods.