Halftone/contone conversion using neural networks

Win Bin Huang, Wei Chen Chang, Yen Wei Lu, Alvin W.Y. Su, Yau Hwang Kuo

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3547-3550
Number of pages4
JournalProceedings - International Conference on Image Processing, ICIP
Volume2
Publication statusPublished - 2004
Event2004 International Conference on Image Processing, ICIP 2004 - , Singapore
Duration: 2004 Oct 182004 Oct 21

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'Halftone/contone conversion using neural networks'. Together they form a unique fingerprint.

Cite this