Composite morphological functions for DT-CNNs

Mathematical morphology is a powerful means to specify image manipulations; discrete-time cellular neural networks (DT-CNN) is the fast realization. The attractive combination has been sufficiently shown for simple problems but tends to fail in efficiency for more complex ones. The paper introduces a complement and argument swap (CAS) equivalence that allows to solve an image processing problem through a small library of representative efficient designs.