EXTREMAL AUTOMATA FOR IMAGE SHARPENING

We study numerically the parallel iteration of Extremal Rules. For fou r Extremal Rules, conceived for sharpening algorithms for image proces sing, we measured, on the square lattice with Von Neumann neighborhood and free boundary conditions, the typical transient length, the loss of information and the damage spreading response considering random an d smoothening random damage. The same qualitative behavior was found f or all the rules, with no noticeable finite sie effect. They have a fa st logarithmic convergence towards the fixed points of the parallel up date. The linear damage spreading response has no discontinuity at zer o damage, for both kinds of damage. Three of these rules produce simil ar effects. We propose these rules as sharpening algorithms for image processing.