A class of nonlinear digital filters, called the threshold
Boolean filter (TBF), is introduced. The TBF is defined
by a Boolean function on the binary domain and is a natural
extension of stack filters. Multilevel representations of a TBF
corresponding to a Boolean function are derived; a TBF can be
represented either as a sum of “local minimum-local maximum”
terms or as an adaptive linear combination of ordered input
data. It is shown that TBF’s may be neither translation invariant
nor scale invariant and that any TBF can be expressed as a
linear combination of stack filters. A subclass of TBF’s, called
linearly separable (LS) TBF’s, defined by the threshold logic is
introduced as a direct extension of weighted-order statistic (WOS)
filters. Implementation and design of a TBF and an LS TBF is
investigated. The procedure for designing TBF’s (LS TBF’s) is
shown to be considerably simpler than designing stack (WOS)
filters, and the former can outperform the latter at marginal
increase in computational cost. Finally, experimental results are
presented to illustrate the performance characteristics of TBF’s
and LS TBF’s.