6 April 1995 Optimal fusion of TV and infrared images using artificial neural networks
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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.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas Fechner, Thomas Fechner, Grzegorz Godlewski, 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); doi: 10.1117/12.205203; https://doi.org/10.1117/12.205203


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