title: Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data.
abstract: We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.
authors.affiliation: Department of Signal Processing, Tampere University of Technology, Tampere, Finland. alessandro.foi@tut.fi
authors.forename: Alessandro
authors.initials: A
authors.lastname: Foi
authors.forename: Mejdi
authors.initials: M
authors.lastname: Trimeche
authors.forename: Vladimir
authors.initials: V
authors.lastname: Katkovnik
authors.forename: Karen
authors.initials: K
authors.lastname: Egiazarian
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity