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Image fusion method based on saliency computation and adaptive PCNN

YANG Xiulin1, HUANG Shuo2*, DENG Miao1,  ZHANG Jihong1,3   

  1. 1. College of Information Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China;
    2. School of Mathematics and Computer Science, Xinyang Vocational and Technical College, Xinyang 464000, Henan, China;
    3.  Shenzhen Key Laboratory of Visual Media Processing and Transmission,
    Shenzhen Institute of Information Technology, Shenzhen 518029, Guangdong, China
  • Received:2013-06-28 Online:2014-04-20 Published:2013-06-28

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Abstract: Simple average processing of low-pass subbands was usually adopted in multi-scale transform based image fusion, which could not protect the saliency information in source images very well. To solve this problem, an image fusion method using the saliency computation to drive adaptive pulse-coupled neural network (PCNN) was proposed to optimize the low-pass subbands fusion. First, source images were decomposed by morphological un-decimated wavelet, and low-frequency coefficients and high-frequency coefficients were obtained. Second, low-frequency coefficients were fused by rule based on saliency computation and PCNN, and high-frequency coefficients were selected by strong absolute value. Finally, the fusion image was got by inverse transform. Experimental results indicated that saliency information of source images was obtained to some extent and the fusion indicators, such as  mutual information, entropy, average gradient and edge preservation degree were improved.

Key words: image fusion, adaptive, multi-scale, morphological un-decimated wavelet, saliency computation, PCNN

CLC Number: 

  • TP391
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