TY - JOUR
T1 - Neural network based method for image halftoning and inverse halftoning
AU - Huang, Win Bin
AU - Su, Alvin W.Y.
AU - Kuo, Yau Hwang
N1 - Funding Information:
This paper is based partially on work supported by the National Science Council (NSC) of Taiwan, R.O.C., under grant No. NSC95-222-E-006-371.
PY - 2008/5
Y1 - 2008/5
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38649107015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38649107015&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2007.04.013
DO - 10.1016/j.eswa.2007.04.013
M3 - Article
AN - SCOPUS:38649107015
SN - 0957-4174
VL - 34
SP - 2491
EP - 2501
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 4
ER -