ADAPTIVE LMS L-FILTERS FOR NOISE SUPPRESSION IN IMAGES

Several adaptive least mean squares (LMS) L-filters, both constrained and unconstrained ones, are developed for noise suppression in images and being compared in this paper. First, the location-invariant LMS L- filter for a nonconstant signal corrupted by zero-mean additive white noise is derived. It is demonstrated that the location-invariant LMS L -filter can be described in terms of the generalized linearly constrai ned adaptive processing structure proposed by Griffiths and Jim. Subse quently, the normalized and the signed error LMS L-filters are studied . A modified LMS L-filter with nonhomogeneous step-sizes is also propo sed in order to accelerate the rate of convergence of the adaptive L-f ilter. Finally, a signal-dependent adaptive filter structure is develo ped to allow a separate treatment of the pixels that are close to the edges from the pixels that belong to homogeneous image regions.