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  • Title: Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data.
    Author: Foi A, Trimeche M, Katkovnik V, Egiazarian K.
    Journal: IEEE Trans Image Process; 2008 Oct; 17(10):1737-54. PubMed ID: 18784024.
    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.
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