A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving pro-
perties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression
estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering
and denoising algorithms such as bilateral and steering kernel filters. It is shown that vertically weighted filters can be
realized by a structure of three interconnected radial basis function (RBF) networks. We also assess the performance of the
algorithm by studying industrial images.
