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Multi-focus Image Fusion Based on the Improved PCNN and Guided Filter

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Abstract

This paper proposes a novel multi-focus image fusion method based on pulse coupled neural networks (PCNN) and guided filter. PCNN matches human visual perception very well. And guided filter is an edge-preserving filter which is proposed in recent years. In our method, the fusion process consists of the following steps: firstly, the source images are preliminarily fused with the guided filter. Then the intermediate fused image is employed to motivate the improved PCNN to generate a fusion map. Finally, the source images are fused according to the fusion map. Six contrast methods are employed to evaluate the performance of the proposed approach in six groups of experiments. The experimental results show that the proposed method outperforms the most existing methods in both subjective visual effect and objective evaluation criteria.

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Acknowledgments

We thank the associate editor and the reviewers for their helpful and constructive suggestions. The authors also would like to express the profound thanks to Zheng Liu for his generous help. This work was jointly supported by China Postdoctoral Science Foundation (Grant No. 2013M532097), Fundamental Research Funds for the Central Universities (lzujbky-2014-52), National Science Foundation of China (Grant Nos. 61201421 & 61175012), and Science Foundation of Gansu Province of China (Grant No. 1208RJYA058).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Zhaobin Wang & Shuai Wang

  2. Institute of Biology, Gansu Academy of Sciences, Lanzhou, 730000, China

    Ying Zhu

Authors
  1. Zhaobin Wang
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  2. Shuai Wang
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  3. Ying Zhu
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Correspondence to Zhaobin Wang.

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Wang, Z., Wang, S. & Zhu, Y. Multi-focus Image Fusion Based on the Improved PCNN and Guided Filter. Neural Process Lett 45, 75–94 (2017). https://doi.org/10.1007/s11063-016-9513-2

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  • DOI: https://doi.org/10.1007/s11063-016-9513-2

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