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Image sensor noise profiling by voting based curve fitting

The output quality of an image filter for reducing noise without damaging the underlying signal, strongly depends on the accuracy of the noise model in characterizing the noise introduced by the acquisition device. In this paper we provide a solution for characterizing signal dependent noise injected at shot time by the image sensor. Different fitting models describing the behavior of noise samples are analyzed, with the aim of finding a model that offers the most accurate coverage of the sensor noise under any of its operating conditions. The noise fitting equation minimizing the residual error is then identified. Moreover, a novel algorithm able to obtain the noise profile of a generic image sensor without the need of a controlled environment is proposed. Starting from a set of heterogeneous CFA images, by using a voting based estimator, the parameters of the noise model are estimated.