Signal-dependent noise modeling for adaptive multiresolution local-statistics filtering

In this paper, a class of signal-dependent noise models that are encountered in image processing applications is considered. Such models are uniquely defined by the gamma exponent, which rules the dependence on the signal, and by the variance of a zero-mean random noise process. An automatic procedure for measuring the model parameters directly from noisy images is presented. Then, adaptive filtering is applied in a multiresolution fashion, to take advantage of increasing SNR of the data at decreasing resolution. A rational Laplacian pyramid is generalized to the noise model to yield signal-independent noise on its layers. Experiments show a high accuracy of results, both of noise estimation and of filtering.