Neural networks for halftoning of color images

This paper illustrates the use of Ilopficld neural networks to halftone color images. We define an error function which is the weighted sum of squared errors of the Fourier components of the original and halftoned images. The weights can be chosen to match the human visual system or other input/output transfer functions. The error function is minimized by using a neural network and solving its dynamical equation iteratively. FFTs are used to perform the necessary convolutions so that the computational requirements arc reasonable even for large images.