HALFTONE/CONTONE CONVERSION USING NEURAL NETWORKS (WP-P9)

Author(s) : Win-Bin Huang (Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan) Yen-Wei Lu (Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan) Yau-Hwang Kuo (Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan) Alvin W.Y. Su (Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan) Wei-Chen Chang (Dept. of Computer Science and Information Engineering, National Cheng Kung University, Taiwan)

Abstract : A novel neural network based method for halftoning and inverse halftoning of digital images is presented. We first start from inverse half-toning of images produced from error diffusion methods using a RBF Network plus a MLP network. The restored contone images have had good quality already. Then, a SLP neural network is used to refine the halftoning processing and the training process of the inverse half-toning network is also involved. The combined training procedure produces half-tone images and the corresponding continuous tone images at the same time. It is found that these contone images have even better PSNR performance. Furthermore, the resulted half-tone images are visually sharper and clearer, too. The proposed inverse half-toning method is also compared to the well-known LUT method.

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