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Image fusion based on median filters and SOFM neural networks: a three-step scheme

Zhang, Z.-L., Sun, S.-H. and Zheng, F.-C. (2001) Image fusion based on median filters and SOFM neural networks: a three-step scheme. Signal Processing, 81 (6). pp. 1325-1330. ISSN 0165-1684

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To link to this article DOI: 10.1016/S0165-1684(00)00273-5

Abstract/Summary

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:18700
Uncontrolled Keywords:Median filter, Self-organizing feature map neural network, Image data fusion
Publisher:Elsevier

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