论文标题
Pogain：配对样品的泊松高斯图像噪声建模
PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples
论文作者
论文摘要
图像噪声通常可以准确地拟合到泊松高斯分布中。但是，仅从嘈杂的图像估算分布参数是一项具有挑战性的任务。在这里，我们研究了可以访问配对的嘈杂和无噪声样品时的情况。当前没有任何方法可以利用无噪声信息，这可能有助于实现更准确的估计。为了填补这一空白，我们从配对的图像样本中得出了一种用于泊松高斯噪声建模的新型，基于累积的方法。我们展示了其在不同基线的性能上的提高，并特别强调了MSE，离群值的效果，图像依赖性和偏见。我们还得出了进一步见解的对数可能性函数，并讨论了现实世界的适用性。
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.
