Optimal fusion of TV and infrared images using artificial neural networks

Thomas Fechner; Grzegorz Godlewski

doi:10.1117/12.205203

6 April 1995 Optimal fusion of TV and infrared images using artificial neural networks

Thomas Fechner, Grzegorz Godlewski

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205203
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

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.

Citation Download Citation

Thomas Fechner and Grzegorz Godlewski "Optimal fusion of TV and infrared images using artificial neural networks", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205203

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