Automatic construction of tree-structural image transformations using genetic programming

We previously proposed an automatic construction method of image transformations. In this method, we approximate an unknown image transformation by a series of several known image filters, and a genetic algorithm optimizes their combination to meet the processing purpose presented by sets of original and target images. In this paper, we propose an extended method named "automatic construction of tree-structural image transformations (ACTIT)". In this new method, a tree whose interior nodes are image filters and leaf ones are input images approximates the transformation. The structures of the trees are optimized using genetic programming. ACTIT finds practical filter combinations that are too complicated to be designed by hand. It is applied to various kinds of image processing tasks. We show examples of its application and verify its validity.