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Technique for Image Fusion Based on PCNN and Convolutional Neural Network

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Book cover Advances in Internetworking, Data & Web Technologies (EIDWT 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 6))

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Abstract

Image fusion has been a hotspot in the area of image processing. How to extract and fuse the main and detailed information as accurately as possible from the source images into the single one is the key to resolving the above problem. Convolutional neural network (CNN) has been proved to be an effective tool to cope with many issues of image processing, such as image classification. In this paper, a novel image fusion method based on pulse-coupled neural network (PCNN) and CNN is proposed. CNN is used to obtain a series of convolution and linear layers which represent the high-frequency and low-frequency information, respectively. The traditional PCNN is improved to be responsible for selecting the coefficients of the sub-images. Experimental results indicate that the proposed method has obvious superiorities over the current main-streamed ones in terms of fusion performance and computational complexity.

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Acknowledgements

The authors thank all the reviewers and editors for their valuable comments and works. The work was supported in part by the National Natural Science Foundations of China under Grant 61309008 and 61309022, in part by Natural Science Foundation of Shannxi Province of China under Grant 2014JQ8349, in part by Foundation of Science and Technology on Information Assurance Laboratory under Grant KJ-15-102, and the Natural Science Foundations of the Engineering University of the Armed Police Force of China under Grant WJY-201414.

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, 710121, China

    Weiwei Kong

  2. Department of Electronics Technology, Engineering University of Armed Police Force, Xi’an, 710086, China

    Yang Lei

  3. Key Laboratory of Information Assurance Technology, Beijing, 100072, China

    Jing Ma

Authors
  1. Weiwei Kong
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  2. Yang Lei
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  3. Jing Ma
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Corresponding author

Correspondence to Weiwei Kong .

Editor information

Editors and Affiliations

  1. Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. School of Computer Sciences, Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  3. Department of Electronic Technology, Key, Engineering University of CAPF, Xi’an, Xizang, China

    Xu An Wang

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Kong, W., Lei, Y., Ma, J. (2018). Technique for Image Fusion Based on PCNN and Convolutional Neural Network. In: Barolli, L., Zhang, M., Wang, X. (eds) Advances in Internetworking, Data & Web Technologies. EIDWT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-59463-7_38

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  • DOI: https://doi.org/10.1007/978-3-319-59463-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59462-0

  • Online ISBN: 978-3-319-59463-7

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