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Proceedings Paper

Optimal fusion of TV and infrared images using artificial neural networks
Author(s): Thomas Fechner; Grzegorz Godlewski
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Paper Abstract

This paper describes the application of a neural network for pixel-level fusion of visible (TV) and infrared (FLIR) images taken from the same scene. The goal of image fusion is to produce a single composite image in which information of interest from both input images is retained. Therefore, the applied fusion method should preserve those image details that are most relevant for human perception while suppressing noise. The proposed fusion method exploits the pattern recognition capabilities of artificial neural networks. Moreover, the learning capability of neural networks makes it feasible to customize the image fusion process. Some experimental results are presented and compared with existing image fusion methods.

Paper Details

Date Published: 6 April 1995
PDF: 7 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205203
Show Author Affiliations
Thomas Fechner, Daimler-Benz AG (Germany)
Grzegorz Godlewski, Daimler-Benz AG (Germany)


Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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