  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.
