  In this paper, we present an algorithm for identifying a parametrically
described destructive unknown system based on a non-gaussianity measure. It is
known that under certain conditions the output of a linear system is more
gaussian than the input. Hence, an inverse filter is searched, such that its
output is minimally gaussian. We use the kurtosis as a measure of the
non-gaussianity of the signal. A maximum of the kurtosis as a function of the
deconvolving filter coefficients is searched. The search is done iteratively
using the gradient ascent algorithm, and the coefficients at the maximum point
correspond to the inverse filter coefficients. This filter may be applied to
the distorted signal to obtain the original undistorted signal. While a similar
approach has been used before, it was always directed at a particular kind of a
signal, commonly of impulsive characteristics. In this paper a successful
attempt has been made to apply the algorithm to a wider range of signals, such
as to process distorted audio signals and destructed images. This innovative
implementation required the revelation of a way to preprocess the distorted
signal at hand. The experimental results show very good performance in terms of
recovering audio signals and blurred images, both for an FIR and IIR distorting
filters.
