We propose a Cellular Nonlinear Network (CNN) ruled by reaction-diffusion equations for quality control by
artificial visual inspection. We show that, using a specific nonlinearity allows to extract regions of interest in
a noisy and weakly contrasted image without any processing time setting. We finally present the electronic
realization of an elementary cell of the CNN for a possible electronic integration.
In this paper we revisit the asymmetric binary channel from the double point of view of detection theory and information theory. We first evaluate the capacity of the asymmetric binary channel as a function of the probabilities of false alarm and of detection, thus allowing a noise distribution independent analysis. This sets the a priori probabilities of the hypotheses and couples the two points of view. We then study the simple realization of the asymmetric binary channel using a threshold device. We particularly revisit noise-enhanced processing for subthreshold signals using the aforementioned parametrization of the capacity, and we report a somewhat paradoxical effect: using the channel at its capacity precludes in general an optimal detection.