Image halftoning and reconstruction using a neural network

Digital image halftoning is treated as an optimization problem to which neural networks provide an efficient parallel solution. An image distortion measure is introduced in which the gray-tone image is approximated by a filtered version of the halftoned image. This distortion measure is minimized by using a near-neighborhood-connected symmetric neural network. The filter used in the distortion measure is estimated on the basis of a training set of gray-tone and bilevel images. This filter is then used for the reconstruction of a gray-tone image from its halftoned version. The above procedure, combined with a postprocessing of the reconstructed image by a nonlinear edge-preserving noise-smoothing filter, provides images of good quality