A Genetic Programming Based Scheme for Combining Image Operators

Sophisticated image processing is usually nonlinear and difficult to model. In addition to the conventional image processing tools, we need some alternatives to bridge the gap between low-level and semantic level computation. This paper presents an idea of image processing scheme. We transform an image into different representations; feed the representations to the proper cellular automaton (CA) components to produce the information images; use the information images as the inputs to the combination program; and finally get the processed result. To identify the needed transforms, the CA transition rules, and the combination expression, we adopt genetic programming (GP) and cellular programming (CP) to search for the configuration. The searched configuration separates the parallelizable and sequential parts of the program. We don't enforce the linearity of the program, and it is likely that the searched result matches to the nonlinear nature of human semantics.