Image halftoning and reconstruction using a neural network

Kollias, Stefanos; Tsai, Tu-Chih; Anastassiou, Dimitris

dc.contributor.author	Kollias, Stefanos	en
dc.contributor.author	Tsai, Tu-Chih	en
dc.contributor.author	Anastassiou, Dimitris	en
dc.date.accessioned	2014-03-01T02:40:55Z
dc.date.available	2014-03-01T02:40:55Z
dc.date.issued	1989	en
dc.identifier.issn	07367791	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30275
dc.subject	Digital Image	en
dc.subject	Optimization Problem	en
dc.subject	Neural Network	en
dc.subject.other	Signal Filtering and Prediction	en
dc.subject.other	Systems Science and Cybernetics--Neural Nets	en
dc.subject.other	Bilevel Images	en
dc.subject.other	Gray-Tone Image	en
dc.subject.other	Image Distortion Measure	en
dc.subject.other	Image Halftoning	en
dc.subject.other	Noise-Smoothing Filter	en
dc.subject.other	Image Processing	en
dc.title	Image halftoning and reconstruction using a neural network	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICASSP.1989.266797	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICASSP.1989.266797	en
heal.publicationDate	1989	en
heal.abstract	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 the above procedure, combined with a postprocessing of the reconstructed image by a nonlinear edge-preserving noise-smoothing filter, provides images of good quality.	en
heal.publisher	Publ by IEEE, Piscataway, NJ, United States	en
heal.journalName	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings	en
dc.identifier.doi	10.1109/ICASSP.1989.266797	en
dc.identifier.volume	3	en
dc.identifier.spage	1787	en
dc.identifier.epage	1790	en