A Spatial Domain Sigma-Delta Modulation via Discrete-Time Cellular Neural Networks

Abstract

In this paper, a novel spatial domain sigma-delta modulation using two-layered discrete-time cellular neural networks (DT-CNNs) is proposed. Since the nature of CNN dynamics with the output function which has two saturation regions is to binarize the input image, the dynamics has a capabilities for a digital image halftoning. In the proposed architecture, the nonlinear interpolative dynamics is exploited to obtain an optimal reconstruction image from the bilevel modulated image, and quantization noises are spatially distributed by the noise shaping property of the dynamics. The experimental results show a excellent reconstruction performance and capabilities of the CNN as a sigma-delta modulation.