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Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data

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4 Author(s)
Alessandro Foi ; Dept. of Signal Process., Tampere Univ. of Technol., Tampere ; Mejdi Trimeche ; Vladimir Katkovnik ; Karen Egiazarian

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.

Published in:

IEEE Transactions on Image Processing  (Volume:17 ,  Issue: 10 )