Nonlinear filtering of multivariate images under robust error criterion

A class of nonlinear filters for multivariate data is introduced. A robust error criterion is minimized. Approximate algorithms for computing the filter output are developed. Polynomial signal model is used in applications where the signal amplitude has to be retained with high fidelity. Simulated data and RGB color image data are used in experiments.