Cellular Automata for Elementary Image Enhancement

Abstract

We study various cellular automata as algorithms for elementary image enhancement, which refers to methods used to improve features of an image without previous information about them that can be implemented by straightforward techniques. Cellular automata appear as natural tools for image processing due to their local nature and simple parallel computer implementation. For this reason various cellular automata algorithms for sharpening and smoothing are presented and studied in this context. Their dynamical behavior is characterized for sequential and parallel updating by associating to them strictly decreasing functionals with the dynamics of the automata. Since tight bounds for the transient time usually cannot be obtained from these operators, a numerical study is needed to analyze their typical performance and effects. For this purpose we compare them with the classical methods for real two dimensional images in terms of convergence rate, effects, and stability in front of noise. The cellular automata methods studied present very fast convergence to fixed points, noise stability, and improvements on real images, which are features that allow us to propose them as a first level elementary image enhancement.