  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.
