Performance analysis of a McClellan transformation-based 2-D adaptive digital filter

A two-dimensional (2-D) adaptive filter structure based on the McClellan transformation design technique for 2-D finite impulse response (FIR) filters is capable of achieving improved convergence rates and reduced computational efficiency in 2-D adaptive filters. It has been shown experimentally that if the transformation structure is constrained by prior knowledge of contour shapes in the frequency domain, the 2-D adaptive algorithm greatly reduces computational requirements and produces more rapid learning characteristics, as compared to the 2-D direct form. The authors present an analysis of the McClellan transformation 2-D adaptive filter to illustrate that the learning characteristics are similar to those of a 1-D last mean square (LMS) adaptive filter