PoGaIN：基于成对样本的泊松-高斯图像噪声建模
PoGaIN: Poisson-Gaussian Image Noise Modeling from Paired Samples
摘要：
图像噪声通常可以精确地拟合到泊松-高斯分布。然而，估计的分布参数，从噪声图像是一个具有挑战性的任务。在这里，我们研究的情况下，配对的噪声和无噪声的样本是可访问的。目前没有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，我们推导出一种新的，基于累积量的方法，从成对的图像样本的泊松-高斯噪声建模。我们展示了它在不同基线上的改进性能，特别强调MSE，离群值的影响，图像依赖性和偏差。我们还推导出对数似然函数，以进一步了解并讨论现实世界的适用性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
