<title>Spectral approach to selection probabilities of stack filters</title>

Abstract

Spectral approaches to the selection probabilities of stack filters are derived. The spectral algorithms are given for the computation of the rank and sample selection probability vectors. They have computational complexity O(2N), where N is the number of input samples within the window. The main advantage of the spectral algorithms to the nonspectral ones is that spectral algorithms are universal in the sence that the complexities of these algorithms are independent on the logical function used as the base for stack filtering. They are also straightforward to implement and fast spectral transforms exist.