State of the art of morphological and nonlinear digital image processing

A multitude of nonlinear digital image processing techniques has appeared in the literature. The following classes of nonlinear digital image/signal processing techniques can be identified at present: (1) order statistic filters; (2) homomorphic filters; (3) polynomial filters; (4) mathematical morphology (5) neural networks; (6) nonlinear image restoration. One of the main current limitations of nonlinear techniques is the lack of unifying theory, that can encompass all existing nonlinear filter classes. Each class of nonlinear processing techniques possesses its own mathematical tools that can provide reasonably good analysis of its performance. Cross-fertilization of these classes has been proven to be promising. For example, mathematical morphology and order statistic filters have been efficiently integrated in one class, although they come from completely different origins. The authors focus on the morphological and order statistic filters their properties and especially on trends in these two areas