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IEEE Trans Image Process. 2008 Oct;17(10):1737-54. doi: 10.1109/TIP.2008.2001399.

Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data.

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Department of Signal Processing, Tampere University of Technology, Tampere, Finland.


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.

[Indexed for MEDLINE]

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