Neural network based method for image halftoning and inverse halftoning

  • Authors:

  • Win-Bin Huang;Alvin W. Y. Su;Yau-Hwang Kuo

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan, Taiwan, ROC;Department of Computer Science and Information Engineering, National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

Quantified Score

Hi-index 12.06

Visualization

Abstract

A hybrid neural network based method for halftoning and inverse halftoning of digital images is presented. The halftone image is performed by single-layer perceptron neural network (SLPNN), and its corresponding continuous-tone image is reconstructed by radial-basis function neural network (RBFNN). The combined training procedure produces halftone images and the corresponding continuous tone images at the same time. The PSNR performance and visual image quality of these contone images achieved is comparable to the well-known inverse halftoning methods. The resultant halftone images compared with the error diffusion halftone are visually good, too. Furthermore, we apply different kinds of halftone images to a bi-level image compression method, called Block Arithmetic Coding for Image Compression (BACIC), which is better than the current facsimile methods.