CNN architectures for constrained diffusion based locally adaptive image processing

Abstract

AbstractIn this paper, a cellular neural network (CNN) based locally adaptive scheme is presented for image segmentation and edge detection. It is shown that combining a constrained (linear or non‐linear) diffusion approach with adaptive morphology leads to a robust segmentation algorithm for an important class of image models. These images are comprised of simple geometrical objects, each having a homogeneous grey‐scale level and they might be overlapping. The background illumination is inhomogeneous, the objects are corrupted by additive Gaussian noise and possibly blurred by low‐pass‐filtering‐type effects. Typically, this class has a multimodal (in most cases bimodal) image histogram and no special (easily exploitable) characteristics in the frequency domain. The synthesized analogic (analog and logic) CNN algorithm combines a diffusion‐type filtering with a locally adaptive strategy based on estimating the first‐order (mean) and second‐order (variance) statistics. Both PDE‐ and non‐PDE‐related diffusion schemes are examined and compared in the CNN framework. It is shown that the proposed algorithm with various diffusion‐type filters offers a more robust solution than some globally optimal thresholding schemes. All algorithmic steps are realized using nearest‐neighbour CNN templates. The VLSI implementation complexity and some robustness issues are carefully analysed and discussed in detail. A number of tests have been completed on original and artificial grey‐scale images. Copyright © 2002 John Wiley & Sons, Ltd.