Digital sensors can lead to noisy results under many circumstances. To be able to remove the undesired noise from images, proper noise modeling and an accurate noise parameter estimation is crucial. In this project, we use a Poisson-Gaussian noise model for the raw-images captured by the sensor, as it fits the physical characteristics of the sensor closely. Moreover, we limit ourselves to the case where observed (noisy), and ground-truth (noise-free) image pairs are available. Using such pairs is beneficial for the noise estimation and is not widely studied in literature. Based on this model, we derive the theoretical maximum likelihood solution, discuss its practical implementation and optimization. Further, we propose two algorithms based on variance and cumulant statistics. Finally, we compare the results of our methods with two different approaches, a CNN we trained ourselves, and another one taken from literature. The comparison between all these methods shows that our algorithms outperform the others in terms of MSE and have good additional properties.

翻译：数字传感器在许多情况下可能导致噪音。 能够从图像中去除不理想的噪音, 适当的噪音建模和准确的噪音参数估计至关重要 。 在这个项目中, 我们使用 Poisson- Gausian 噪音模型来制作传感器拍摄的原始图像, 因为它与传感器的物理特征非常接近 。 此外, 我们仅限于被观测到的( 噪音) 和地面真实( 无噪音) 图像配对的情况 。 使用这些配对有利于噪音估计, 并且没有在文献中进行广泛研究 。 基于这个模型, 我们得出理论上的最大可能性解决方案, 讨论其实际实施和优化 。 此外, 我们根据差异和累积性统计数据提出两种算法。 最后, 我们用两种不同的方法来比较我们方法的结果, 一种是我们训练过的CNN, 另一种是从文献中取的。 所有这些方法之间的比较表明, 我们的算法在MSE方面优于其他方法, 并且具有良好的额外特性 。