PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples

• URL: http://arxiv.org/abs/2210.04866v1
• Date: Mon, 10 Oct 2022 17:34:49 GMT
• Title: PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples
• Authors: Nicolas B\"ahler, Majed El Helou, \'Etienne Objois, Kaan Okumu\c{s}, and Sabine S\"usstrunk
• Abstract summary: 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.
• Score: 9.22047303381213
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from only a noisy image is a challenging task. Here, we study the case when paired noisy and noise-free samples are available. No method is currently available to exploit the noise-free information, which holds the promise of achieving more accurate estimates. 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, and additionally derive the log-likelihood function for further insight and discuss real-world applicability.

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