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.

翻译：图像噪音通常可以精确地与Poisson-Gausian的分布相匹配。 然而,从噪音图像中估计分布参数是一项艰巨的任务。 在这里,我们研究的是能够获取对齐的噪音和无噪音样本的案例。 目前没有办法利用无噪音信息,这可能有助于实现更准确的估计。为了填补这一空白,我们从配对图像样本中为Poisson-Gausian的噪音建模开发出一种新颖的、基于积聚性的方法。我们展示了它在不同基线中的性能,特别侧重于MSE,外层效应、图像依赖性和偏向性。我们还得出了类似日志的功能,用于进一步深入了解和讨论现实世界的适用性。