Chaotic methods for image processing

We extend methods for processing chaotic time series to two-dimensional images. The motivation is to develop new tools for understanding physical systems that can be imaged, such as the ocean surface. The novel issue addressed is the computation of the average mutual information for images. The average mutual information provides insights into energy transport and information loss rates in the underlying system. It also provides a crucial parameter needed for further processing with chaotic methods.