Adaptive Multichannel Marginal L--lters

Three adaptive multichannel L-lters based on marginal data ordering are proposed. They rely on well-known algorithms for the iterative minimization of the mean square error (MSE), namely, the least mean squares (LMS), the normalized LMS (NLMS), and the LMS-Newton (LMSN) algorithms. We treat both the unconstrained minimization of the MSE and the minimization of the MSE when structural constraints are imposed on the lter coeecients. The performance of the proposed adaptive multichannel L-lters is compared to that of other multi-variate nonlinear lters in color image ltering. Adaptive multichannel linear lters and adaptive single-channel L-lters are considered as well. Performance comparisons are made in both RGB and U V W color spaces. The proposed adaptive multichannel L-lters outperform the other candidates in noise suppression for color images corrupted by mixed impulsive and additive white contaminated Gaussian noise.