Bayesian multilevel random‐effects model for estimating noise in image sensors. Issue 12 (4th September 2020)

• Record Type: Journal Article
• Authors: Riutort‐Mayol, Gabriel; Gómez‐Rubio, Virgilio; Marqués‐Mateu, Ángel; Lerma, José Luis; López‐Quílez, Antonio
• Is Part Of: IET image processing. Volume 14:Issue 12(2020)
• Page Start: 2737
• Page End: 2745
• Publication Date: 2020-09-04
• Subjects: probability -- Bayes methods -- image sensors; Bayesian multilevel random‐effects model -- image sensors -- sensor noise sources -- single image -- multiple images -- Bayesian approach -- Bayesian probabilistic model -- different reflectance -- image sensing model -- complex model -- fully probabilistic model -- Bayesian modelling framework -- noise parameters -- model predictions -- imaging process; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5926
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.