# PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples

arXiv: https://arxiv.org/abs/2210.04866 · HTML: https://mlsanity.com/paper/2210.04866/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images](https://mlsanity.com/paper/1812.10366/) (2018)
- [Model-blind Video Denoising Via Frame-to-frame Training](https://mlsanity.com/paper/1811.12766/) (2018)
- [Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning](https://mlsanity.com/paper/1907.03029/) (2019)
- [Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks](https://mlsanity.com/paper/2003.07119/) (2020)
- [BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration](https://mlsanity.com/paper/2011.01406/) (2020)
- [Non-Convex Weighted Lp Minimization based Group Sparse Representation Framework for Image Denoising](https://mlsanity.com/paper/1704.01429/) (2017)
- [Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion](https://mlsanity.com/paper/2101.04631/) (2021)

## Built on by (most cited first, 5 of 5)

- [Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising](https://mlsanity.com/paper/2503.11262/) (2025)
- [2-Shots in the Dark: Low-Light Denoising with Minimal Data Acquisition](https://mlsanity.com/paper/2512.03245/) (2025)
- [Target Aware Poisson-Gaussian Noise Parameters Estimation from Noisy Images](https://mlsanity.com/paper/2210.12142/) (2022)
- [Zero-Shot Image Denoising for High-Resolution Electron Microscopy](https://mlsanity.com/paper/2406.14264/) (2024)
- [Speech2Grasp: Data-Efficient Transfer of Text-Conditioned Grasp Detection to Speech in Humanoid Robots](https://mlsanity.com/paper/2607.26567/) (2026)

## Abstract (arXiv)

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. No method is currently available to exploit the noise-free information, which may help to achieve more accurate estimations. To fill this gap, we derive a novel, cumulant-based, approach for Poisson-Gaussian noise modeling from paired image samples. We show its improved performance over different baselines, with special emphasis on MSE, effect of outliers, image dependence, and bias. We additionally derive the log-likelihood function for further insights and discuss real-world applicability.