  Sensor noise sources cause differences in the signal recorded across pixels
in a single image and across multiple images. This paper presents a Bayesian
approach to decomposing and characterizing the sensor noise sources involved in
imaging with digital cameras. A Bayesian probabilistic model based on the
(theoretical) model for noise sources in image sensing is fitted to a set of a
time-series of images with different reflectance and wavelengths under
controlled lighting conditions. The image sensing model is a complex model,
with several interacting components dependent on reflectance and wavelength.
The properties of the Bayesian approach of defining conditional dependencies
among parameters in a fully probabilistic model, propagating all sources of
uncertainty in inference, makes the Bayesian modeling framework more attractive
and powerful than classical methods for approaching the image sensing model. A
feasible correspondence of noise parameters to their expected theoretical
behaviors and well calibrated posterior predictive distributions with a small
root mean square error for model predictions have been achieved in this study,
thus showing that the proposed model accurately approximates the image sensing
model. The Bayesian approach could be extended to formulate further components
aimed at identifying even more specific parameters of the imaging process.
