Two-dimensional signal filters under modeling uncertainties

Matched and Wiener filters are considered for two-dimensional signal processing applications when the a priori information about signal and noise characteristics are not completely specified. This work is an extension of previous work on robust filters for one-dimensional processing. The approach is to design filters which are saddle-point or mini-max solutions for the criterion functional (mean-squared-error or signal-to-noise ratio) over the classes of allowable signal shapes and signal and noise spectral densities.