Recursive implementation of constrained LMS L-filters for image restoration

This paper addresses the problem of designing recursive L-filters optimized by the least mean square (LMS) algorithm. The sorting operation brings in nonlinear behaviour into the L-filter structure, and thus it hampers the de rivation of a closed-form solution for optimizing the weights suited to rec ursive LMS L-filters. An alternative training strategy is proposed to itera tively derive the weighting coefficients for recursive L-filters. Simulatio ns conducted show the advantage of the recursive L-filter over its nonrecur sive counterpart in suppressing image noise. (C) 2001 Elsevier Science B.V. All rights reserved.