Boolean design of binary initialized and coupled CNN image processing operators

As soon as an image processing operator can be expressed as a linearly separable Boolean function involving a cell and its neighborhood, there is a way of straightforwardly deriving an equivalent cellular neural network (CNN) operation. An appropriate method had already been introduced for the robust design of uniformly initialized uncoupled CNN operators, and is now applied to the design of binary initialized and coupled CNN operators. A way of implementing in a unique operator two different Boolean functions conditioning the white-to-black and the black-to-white transitions, respectively, is also presented.