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Proceedings Paper

Noise reduction and image enhancement using a hardware implementation of artificial neural networks
Author(s): Robert David; Erin Williams; Ghislain de Tremiolles; Pascal Tannhof
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Paper Abstract

In this paper, we present a neural based solution developed for noise reduction and image enhancement using the ZISC, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Artificial neural networks present the advantages of processing time reduction in comparison with classical models, adaptability, and the weighted property of pattern learning. The goal of the developed application is image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). Image results show a quantitative improvement over the noisy image as well as the efficiency of this system. Further enhancements are being examined to improve the output of the system.

Paper Details

Date Published: 22 March 1999
PDF: 10 pages
Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); doi: 10.1117/12.343040
Show Author Affiliations
Robert David, IBM France (France)
Erin Williams, IBM France (United States)
Ghislain de Tremiolles, IBM France (France)
Pascal Tannhof, IBM France (France)


Published in SPIE Proceedings Vol. 3728:
Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks
Thomas Lindblad; Mary Lou Padgett; Jason M. Kinser, Editor(s)

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