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Multi-focus image fusion based on L1 image transform

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Abstract

In this paper, a new multi-focus image fusion algorithm based on L1 image transform is proposed. A distinctive advantage of the proposed algorithm is that an edge-preserving image decomposition (EPID) framework is constructed by introducing a L1-norm based image transform, which can not only effectively preserve and sharpen salient edges and ridges while eliminating insignificant details in the smoothing subband, but also maintain the detail information in the detail subbands. Another advantage is that the fusion rules for the smoothing subband and detail subbands are designed respectively according to their own characteristics so that both the structure and detail information can be fully retained. The fusion process mainly consists of the following three steps. Firstly, each source image is decomposed into a smoothing subband and several detail subbands by utilizing the EPID framework. Then, the subbands are fused by different fusion rules respectively to obtain a fused smoothing subband and a series of fused detail subands. Finally, the final fused image is reconstructed with less distortions by synthesizing the fused smoothing subband and a series of fused detail subands. Experimental results demonstrate the superiority of the proposed algorithm in terms of the visual perception and objective assessments.

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Acknowledgements

The work was supported in part by the National Natural Science Foundation of China under Grant 61801190 and 61272209, in part by the Nature Science Foundation of Jilin Province under Grant 20180101055JC, in part by the Outstanding Young Talent Foundation of Jilin Province under Grant 20180520029JH, in part by the China Postdoctoral Science Foundation under Grant 2017M611323, in part by the Industrial Technology Research and Development Funds of Jilin Province under Grant 2019C054-3, and in part by the National Science & Technology Pillar Program of China under Grant 2012BAH48F02.

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Authors and Affiliations

  1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Shuang Yu, Xiongfei Li & Xiaoli Zhang

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Shuang Yu, Xiongfei Li & Xiaoli Zhang

  3. College of Software, Jilin University, Changchun, 130012, China

    Mingrui Ma

  4. CSIRO Data61, Sydney, NSW, 2016, Australia

    Shiping Chen

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  1. Shuang Yu
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  2. Xiongfei Li
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  4. Xiaoli Zhang
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Correspondence to Xiaoli Zhang.

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Yu, S., Li, X., Ma, M. et al. Multi-focus image fusion based on L1 image transform. Multimed Tools Appl 80, 5673–5700 (2021). https://doi.org/10.1007/s11042-020-09877-4

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