IMAGE REPRESENTATION THROUGH GRAY-SCALE ITERATED FUNCTION SYSTEMS

Image representation using the random iterated function system (Random IFS) has a disadvantage in that it requires many iterations. This pap er proposes image representation through a gray-scale iterated functio n system. This method requires fewer iterations and it earn obtain bet ter images than the random IFS. The saving in iterations is realized f irst by the use of a deterministic method, whereas in the random IFS, each mapping is defined as a probablistic event, and it needs many ite rations to satisfy the law of large numbers. In the proposed method, t he reconstructed image converges faster than in the conventional metho d, and it is shown to yield better reconstructed images. Second, stati stical characteristics of the IFS parameters are investigated to be us ed for further accelerating the IFS parameter search. The characterist ics are shown to be stable among different images. Simulation results are included to demonstrate the effectiveness of the accelerated algor ithm.