Overdispersion Parameter Estimation for Negative Binomial Optimization for Low-Photon Imaging

Low-photon imaging is widely applied in many fields such as medical imaging, astronomy, and fluorescence microscopy. Compared to Poisson noise, the negative binomial (NB) distribution provides a more flexible and accurate model for over-dispersed photon-limited data. While NB-based reconstruction and denoising methods have demonstrated improved performance across various scenarios, accurately estimating the over-dispersion parameter r remains a significant challenge. Traditional approaches, such as the method of moments and maximum quasi-likelihood estimation, can struggle with accuracy and robustness. To address this limitation, we propose a deep learning-based framework to predict the over-dispersion parameter r in a data-driven manner. Experiments show that our method is both accurate and generalizable, enabling more effective applications of NB models in real-world imaging tasks.