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Multi-focus image fusion with random walks and guided filters

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Abstract

Multi-focus image fusion technique is able to help obtaining an all-focused image, which is advantage to human vision and image processing. In this paper, a novel multi-focus image fusion method is proposed based on random walk and guided filter. In the proposed method, the decomposition function and the optimizing function of random walk are used in multi-focus image fusion. And the random walk is also utilized for weight maps directly. The advantages of random walk and guided filter in image fusion are fully utilized by regulating proportional coefficients artificially. The proposed method concludes six steps: first, decomposing source images into detail layers and base layers with random walk; second, the random walk is used for weight maps directly and the guided filter is used as smoothing filters to get the streamlined weight maps of the detail layers and the base layers, respectively; third, the weight maps of the detail layers and the base layers are acquired by summing the initializing weight maps in different proportions; and then, the final weight maps of the detail layers are acquired using random walk for optimizing. After that, the fused detail layer and base layer are obtained by weighted average of detail layers and base layers, singly. Finally, the fused image is gained by summing up the fused base layer and the fused detail layer. Experiments demonstrate that the proposed method outperforms many other approaches in both subjective and objective assessments.

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Acknowledgements

We would like to thank the associate editors and the reviewers for their valuable comments and suggestions. The authors also thank Shuai Wang for his generous help.

Funding

This study was funded by National Natural Science Foundation of China (Grant no. 61201421) and the Fundamental Research Funds for the Central Universities of Lanzhou University (lzujbky-2017-187).

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Authors and Affiliations

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

    Zhaobin Wang, Lina Chen & Jian Li

  2. Key Laboratory of Microbial Resources Exploitation and Application of Gansu Province, Institute of Biology, Gansu Academy of Sciences, Lanzhou, 730000, China

    Ying Zhu

Authors
  1. Zhaobin Wang
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  2. Lina Chen
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  3. Jian Li
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  4. Ying Zhu
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Correspondence to Zhaobin Wang or Ying Zhu.

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Communicated by Q. Tian.

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Wang, Z., Chen, L., Li, J. et al. Multi-focus image fusion with random walks and guided filters. Multimedia Systems 25, 323–335 (2019). https://doi.org/10.1007/s00530-019-00608-w

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