A neural network for image smoothing and segmentation

Abstract

Older work of the author on image smoothing and segmentation with graph based parallel-sequential processing structures gave encouraging results but was not able to segment images with very strong noise. Therefore, a modified method was developed. Instead of the used “hard” Pixel Adjacency Graph (PAG) which was used formerly, a “soft” or fuzzy PAG is defined via a degree of adjacency of 4-neighbored pixels. Furthermore, the averaging over 4-neighbors is applied recursively using a nonlinear weighting function which is closely connected with the degree of adjacency and which guarantees efficient noise reduction, edge preserving, and adaptation. The discrete nonlinear dynamic equation system describing the averaging process can be implemented with a Discrete Time Cellular Neural Network (CNN). Its stable states are the smoothed images. Then the soft PAG describing the edge strength’ and the hard PAG defining the segments can be calculated. The method now can cope with strong noise. Some results demonstrate its smoothing and segmenting capability.