Abstract
The image fusion approach based on regional feature is simple and easy to be carried out. However, many edge information of the image is generally neglected in this way, especially in the case of the source images with fuzzy edge. Utilizing the similarity between the fuzzy inference system(FIS) and neural networks(NNs), the paper fuses FIS and NNs to perform the image fusion based on regional deviation to solve the problem. Five membership functions based on Gaussian function are set up in the networks and Genetic Algorithm (GA) is employed to train the networks. The proposed approach can dynamically obtain optimal image fusion weights based on regional features, so as to optimize performance of image fusion. Simulation experiments for image fusion prove the proposed approach far outperforms the traditional image fusion approach based on regional features.
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© 2009 Springer-Verlag Berlin Heidelberg
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Ma, Hm. (2009). Research on Image Fusion Based on Regional Feature and Fuzzy Neural Networks. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_17
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DOI: https://doi.org/10.1007/978-3-642-03664-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03663-7
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