Haar filtering with pyramidal resistive networks

Resistive networks have attracted the interest of researchers for their potential applications especially in parallel signal processing. At present they are essential components in analog VLSI circuit implementation of many image processing systems. Resistive grids, as well as their dynamic and more complex counterpart-the cellular neural networks-can perform various spatial filtering operations. One of the most important applications of the resistive grid is the silicon retina, proposed by Mahowald and Mead [1989], whose structure has a well-established neurobiological basis. The data to be processed (an image in the 2-D case) is sampled by a rectangular grid, so that each node of the network is associated with a pixel in the image that is to be filtered. In this paper we propose a different structure for a resistive network with a pyramidal topology which may prove to find some useful applications in parallel signal processing. We will show that the proposed network performs a nonlinear spatial filtering of the input 1D image, implementing in fact a Haar filter