Abstract
In this paper, an image fusion method using self-constraint pulse coupled neural network (PCNN) is proposed. A self-constraint restrictive function is introduced to PCNN neuron, so that the relation among neuron linking strength, pixel clarity and historical linking strength is adjusted adaptively. Then the pixels of original images corresponding to the fired and unfired neurons of PCNN are considered as target and background respectively, after which new fire mapping images are obtained for original images. Finally, the clear objects of original images are decided by the weighted fusion rule with the fire mapping images and merged into a new image. Experiment result indicates that the proposed method has better fusion performance than several traditional approaches.
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References
Xiaohui, Y., Licheng, J.: Fusion algorithm for remote sensing images based on nonsubsampled contourlet transform. Acta Automatica Sinica 34(3), 274–281 (2008)
Qiang, Z., Baolong, G.: Multifocus image fusion using the nonsubsampled contourlet transform. Signal Processing 89(7), 1334–1346 (2009)
Zhaobin, W., Yide, M.: Medical image fusion using m-PCNN. Information Fusion 9(2), 176–185 (2008)
Xiaobo, Q., Jingwen, Y., Hongzhi, X., et al.: Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Automatica Sinica 34(12), 1508–1514 (2008)
Zhijiang, Z., Chunhui, Z., Zhihong, Z.: A new method of PCNN′s parameter′s optimization. Acta Electronic Asinic 35(5), 996–1000 (2007)
Wang, Y., Vijay, V.J.: Interaction trust evaluation in decentralized environments. In: Proc. of the 5th International Conference on Electronic Commerce and Web Technology, Zaragoza, Spain, pp. 144–153 (2004)
Mingwu, Z., Bo, Y., Wenzheng, Z.: Self-constraint reputation updating model. Computer Engineering 33(18), 145–147 (2007)
Zhaobin, W., Yide, M., Feiyan, C., et al.: Review of pulse-coupled neural networks. Image and Vision Computing 28(1), 5–13 (2010)
Shuyuan, Y., Min, W., Licheng, J., et al.: Image fusion based on a new contourlet packet. Information Fusion 11(2), 78–84 (2010)
Berg, H., Olsson, R., Lindblad, T., et al.: Automatic design of pulse coupled neurons for image segmentation. Neurocomputing 71(10-12), 1980–1993 (2008)
Jiangbo, Y., Houjin, C., Wei, W., et al.: Parameter determination of pulse coupled neural network in image processing. Acta Electronica Sinica 36(1), 81–85 (2008)
Shuyuan, Y., Min, W., Yanxiong, L., et al.: Fusion of multiparametric SAR images based on SW-nonsubsampled contourlet and PCNN. Signal Processing 89(12), 2596–2608 (2009)
Qiguang, M., Baoshu, W.: A novel image fusion algorithm based on local contrast and adaptive PCNN. Chinese Journal of Computers 31(5), 875–880 (2008)
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Jiao, Z., Xiong, W., Xu, B. (2010). Image Fusion Using Self-constraint Pulse-coupled Neural Network. In: Li, K., Jia, L., Sun, X., Fei, M., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science(), vol 6330. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15615-1_74
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DOI: https://doi.org/10.1007/978-3-642-15615-1_74
Publisher Name: Springer, Berlin, Heidelberg
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