Learning topological image transforms using cellular simultaneous recurrent networks

In this work, we investigate cellular simultaneous recurrent networks (CSRNs) to learn topological image mappings, particularly those of the affine transformations. While affine image transformation in conventional image processing is a relatively simple task, learning these transformations is an important part of having neural networks (NNs) function as generalized image processors. We introduce the CSRN and discuss its adaptation for image processing tasks. We report results for translation, rotation and scaling of both binary and grey-scale images. Our results suggest that the CSRN is capable of learning and performing these basic topological transformations.