A neural halftoning algorithm suiting VLSI implementation

Halftoning is presented as an application where the use of simple neural networks proves to be of immediate interest. Halftoning is a nonstandard A/D conversion that is treated as an optimization problem, subject to a frequency-weighted mean-square-error (MSE) criterion. The frequency weight is implemented by means of a specific neural interconnection network based on current diffusion in resistive grids. This physical choice not only leads to a dramatically compact VLSI switch capacitor implementation, but also turns the whole process into a clean 2-D isotropic generalization of Σ-Δ modulation. Isotropy and shift-invariance cooperate within the same halftoning process for the sake of image rendition. The performances prove equal to the deep underlying harmony between the theoretical, algorithmic, and material aspects of the procedure