Bayesian multilevel random-effects model for estimating noise in image sensors

• Published in: IET image processing Vol. 14; no. 12; pp. 2737 - 2745
• Main Authors: Riutort-Mayol, Gabriel, Gómez-Rubio, Virgilio, Marqués-Mateu, Ángel, Lerma, José Luis, López-Quílez, Antonio
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 16.10.2020
• Subjects: Bayes methods; Bayesian approach; Bayesian modelling framework; Bayesian multilevel random‐effects model; Bayesian probabilistic model; complex model; different reflectance; fully probabilistic model; image sensing model; image sensors; imaging process; model predictions; multiple images; noise parameters; probability; Research Article; sensor noise sources; single image; image sensing model; model predictions; imaging process; Bayesian probabilistic model; probability; Bayesian multilevel random-effects model; image sensors; Bayesian approach; fully probabilistic model; sensor noise sources; single image; noise parameters; Bayes methods; multiple images; different reflectance; complex model; Bayesian modelling framework
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2018.5926

Sensor noise sources cause differences in the signal recorded across pixels in a single image and across multiple images. This study presents a Bayesian approach to decomposing and characterising 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 modelling framework more attractive and powerful than classical methods for approaching the image sensing model. A feasible correspondence of noise parameters to their expected theoretical behaviours 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.