Noise reduction and image enhancement using a hardware implementation of artificial neural networks

Robert David; Erin Williams; Ghislain de Tremiolles; Pascal Tannhof

doi:10.1117/12.343040

22 March 1999 Noise reduction and image enhancement using a hardware implementation of artificial neural networks

Robert David, Erin Williams, Ghislain de Tremiolles, Pascal Tannhof

Proceedings Volume 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks; (1999) https://doi.org/10.1117/12.343040
Event: Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks: Neural Networks Fuzzy Systems, Evolutionary Systems and Virtual Re, 1998, Stockholm, Sweden

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.

Citation Download Citation

Robert David, Erin Williams, Ghislain de Tremiolles, and Pascal Tannhof "Noise reduction and image enhancement using a hardware implementation of artificial neural networks", Proc. SPIE 3728, Ninth Workshop on Virtual Intelligence/Dynamic Neural Networks, (22 March 1999); https://doi.org/10.1117/12.343040

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