This thesis describes the theoretical background and practical application of cellular automata with an emphasis on their FPGA implementation. The cellular automaton is a model defined in discrete space which maps states to the points of the space. The state of the cells is updated in a parallel way by a local update rule. Cellular automata can be used in image processing efficiently. In the thesis the solution of two image processing problems with cellular automata is presented: filtering salt & pepper noise and skeletonization. The structure of the cellular automata in both cases is shown. The results of the cellular automata based processing are represented by examples and are analysed quantitatively.
The implementation possibilities of cellular automata on FPGAs and the realization of the concrete CAs are thoroughly reviewed. The architecture and the simulations are represented for both problems and the complete FPGA system is extensively described. The advantages of the FPGA implementation are presented and the benefits of application of cellular automata are summarized.