A complex-valued neuron to transform gray level images to phase information

A system to deal with gray level images applying complex-valued networks has already been proposed. The proposed system combines complex- valued networks with a 2- dimensional discrete Fourier Transform, and is based on the idea of phase matrix image representation. This paper is intended to build pre- processing and post-processing based on network architecture in the system and propose a novel complex- valued neuron to transform gray level images to the phase matrices in the preprocessing. The phase and amplitude of an input for the complex- valued neuron determine its output phase by shifting the input phase by the quantity, which is proportional to the input amplitude. Introducing such neurons enables us easily to deal with gray level images using complex- valued networks. Simulation results on the image representation ability through the preprocessing are also presented.