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Proceedings Paper

Digital Image Halftoning Using Neural Networks
Author(s): Dimitris Anastassiou; Stefanos Kollias
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Paper Abstract

A novel technique for digital image halftoning is presented, performing nonstandard quantization subject to a fidelity criterion. Massively parallel artificial symmetric neural networks are used for this purpose, minimizing a frequency weighted mean squared error between the continuous-tone input and the bilevel output image. The weights of these networks can be selected, so that the generated halftoned images are of good quality. A symmetric formulation of the error diffusion halftoning technique is also presented in the form of a massively parallel network. This network contains a nonmonotonic nonlinearity in lieu of the sigmoid function and is shown to be appropriate for effective halftoning of images.

Paper Details

Date Published: 25 October 1988
PDF: 8 pages
Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.969059
Show Author Affiliations
Dimitris Anastassiou, Columbia University (United States)
Stefanos Kollias, Columbia University (United States)

Published in SPIE Proceedings Vol. 1001:
Visual Communications and Image Processing '88: Third in a Series
T. Russell Hsing, Editor(s)

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