Stochastic-Computational Representation of Self-Similarity for Fractal Coding1

Abstract: A representation of fractal patterns is presented for coding complex random patterns in noisy imagery. Target patterns are assumed to be generated as the attractors of not-yet-identified contraction mappings. For capturing fractal attractor exactly Gaussian array is introduced on continuous image model. By local analysis, most probable points of unknown attractor are extracted within the framework of entropy maximization. In this continuous-discrete representation, background noise is shown to be eliminated via input- and output-filitering. Filtered discrete image is demonstrated to yield computable information enough t