_:n299b451a1b6c48cf87e27db12d75df1fxb48
<http://schema.org/description>
"Sensor noise sources cause differences in the signal recorded across pixels\nin a single image and across multiple images. This paper presents a Bayesian\napproach to decomposing and characterizing the sensor noise sources involved in\nimaging with digital cameras. A Bayesian probabilistic model based on the\n(theoretical) model for noise sources in image sensing is fitted to a set of a\ntime-series of images with different reflectance and wavelengths under\ncontrolled lighting conditions. The image sensing model is a complex model,\nwith several interacting components dependent on reflectance and wavelength.\nThe properties of the Bayesian approach of defining conditional dependencies\namong parameters in a fully probabilistic model, propagating all sources of\nuncertainty in inference, makes the Bayesian modeling framework more attractive\nand powerful than classical methods for approaching the image sensing model. A\nfeasible correspondence of noise parameters to their expected theoretical\nbehaviors and well calibrated posterior predictive distributions with a small\nroot mean square error for model predictions have been achieved in this study,\nthus showing that the proposed model accurately approximates the image sensing\nmodel. The Bayesian approach could be extended to formulate further components\naimed at identifying even more specific parameters of the imaging process."
<https://api.deepai.org/profile/virgilio-gomez-rubio>