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Proceedings Paper

Neural networks for halftoning of color images
Author(s): Daniel T. Ling; Dieter Just
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Paper Abstract

This paper illustrates the use of Hopfield neural networks to halftone color images. We define an error function which is the weighted sum of squared errors of the Fourier components of the original and halftoned images. The weights can be chosen to match the human visual system or other input/output transfer functions. The error function is minimized by using a neural network and solving its dynamical equation iteratively. FFTs are used to perform the necessary convolutions so that the computational requirements are reasonable even for large images.

Paper Details

Date Published: 1 June 1991
PDF: 11 pages
Proc. SPIE 1452, Image Processing Algorithms and Techniques II, (1 June 1991); doi: 10.1117/12.45365
Show Author Affiliations
Daniel T. Ling, IBM/Thomas J. Watson Research Ctr. (United States)
Dieter Just, IBM/Thomas J. Watson Research Ctr. (Germany)

Published in SPIE Proceedings Vol. 1452:
Image Processing Algorithms and Techniques II
Mehmet Reha Civanlar; Sanjit K. Mitra; Robert J. Moorhead II, Editor(s)

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