Adaptive FIR-WOS hybrid filtering

Through the combination of linear finite impulse response (FIR) filters and weighted order statistic (WOS) filters, the authors introduce a new class of nonlinear filters called FIR-WOS hybrid (FWH) filters. This class of filters, including FIR filters, WOS filters, and various kinds of FIR median hybrid (FMH) filters, consists of a few linear FIR subfilters and a WOS filter operated over the outputs of the subfilters. Motivated by the backpropagation algorithm used in neural networks, an adaptive algorithm is derived in the binary domain for determining optimal FWH filters under the mean absolute error criterion. Simulation results in image processing demonstrate that adaptive FWH filters perform as well as linear filters in Gaussian noise and better than adaptive WOS filters in impulsive noise.<<ETX>>