  Cellular Automata (CA) have been considered one of the most pronounced
parallel computational tools in the recent era of nature and bio-inspired
computing. Taking advantage of their local connectivity, the simplicity of
their design and their inherent parallelism, CA can be effectively applied to
many image processing tasks. In this paper, a CA approach for efficient
salt-n-pepper noise filtering in grayscale images is presented. Using a 2D
Moore neighborhood, the classified "noisy" cells are corrected by averaging the
non-noisy neighboring cells. While keeping the computational burden really low,
the proposed approach succeeds in removing high-noise levels from various
images and yields promising qualitative and quantitative results, compared to
state-of-the-art techniques.
