Applications of Emergent Computation in Reaction-Diffusion CNNs for Image Processing

The possibility to exploit emergent computation in a naturally inspired complex network, namely the reaction-diffusion cellular nonlinear network (RD-CNN), is investigated. The particular application under focus is image processing. It is shown that by implementing a simplified discrete-time model and by using the local activity theory to locate potentially useful regions in the huge parameter space, many useful image processing tasks may be performed in reasonable execution time. Such tasks may include but are not limited to: feature extraction, image enhancement, noise removal, pattern formation, etc. A framework is provided for a systematic design allowing the identification of useful genes (sets of parameters) associated with meaningful image processing tasks.