PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples
This addresses a specific challenge in image processing for applications requiring accurate noise modeling, but it is incremental as it builds on existing Poisson-Gaussian frameworks by leveraging paired data.
The paper tackled the problem of estimating Poisson-Gaussian noise distribution parameters from paired noisy and noise-free image samples, and the result was a novel cumulant-based method that showed improved performance over baselines in terms of MSE, outlier effects, image dependence, and bias.
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.