A statistical extension of normalized convolution and its usage for image interpolation and filtering

The natural characteristics of image signals and the statistics of measurement noise are decisive for designing optimal filter sets and optimal estimation methods in signal processing. Astonishingly, this principle has so far only partially found its way into the field of image sequence processing. We show how a Wiener-type MMSE optimization criterion for the resulting image signal, based on a simple covariance model of images or image sequences provides direct and intelligible solution for various, apparently different problems, such as error concealment, or adaption of filters to signal and noise statistics.