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Pulse Coupled Neural Network Based Image Fusion

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Advances in Neural Networks – ISNN 2005 (ISNN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3497))

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Abstract

For the pulse-coupled neural network (PCNN) has an inherent ability to segment images, we present a multisensor image fusion scheme based on PCNN in this paper. The algorithm adopts salience and visibility as two extracted features for each segmented region to determine the fusion weight. Extensive experimental results have demonstrated that the proposed method has extensive application scope and it outperforms the discrete wavelet transform approach, both in visual effect and objective evaluation criteria, particularly when there is movement in the objects or mis-registration of the source images.

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Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China

    Min Li & Zheng Tan

  2. Xi’an Research Inst. of Hi-Tech Hongqing Town, Shaanxi, 710025, China

    Min Li & Wei Cai

Authors
  1. Min Li
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  2. Wei Cai
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  3. Zheng Tan
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China

    Xiao-Feng Liao

  3. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

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© 2005 Springer-Verlag Berlin Heidelberg

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Li, M., Cai, W., Tan, Z. (2005). Pulse Coupled Neural Network Based Image Fusion. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_119

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  • DOI: https://doi.org/10.1007/11427445_119

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-25913-8

  • Online ISBN: 978-3-540-32067-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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