Target Aware Poisson-Gaussian Noise Parameters Estimation from Noisy Images

TL;DR

This paper introduces new algorithms for estimating Poisson-Gaussian noise parameters in raw images using noisy and ground-truth pairs, outperforming existing methods in accuracy.

Contribution

It presents two novel variance and cumulant-based algorithms for noise parameter estimation from image pairs, along with a theoretical maximum likelihood solution.

Findings

Our algorithms outperform CNN and literature methods in MSE.

The proposed methods are effective for noise modeling in raw images.

Algorithms show good robustness and accuracy.

Abstract

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.