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An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy

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A Correction to this article was published on 29 November 2021

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Abstract

Multilevel thresholding image segmentation has always been a popular issue and attracted the attention of many researchers in recent years since it has many applications. However, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To address this problem, in this paper, the optimal thresholds for multilevel thresholding image segmentation are obtained by maximizing the Rényi entropy using the Lévy flight firefly algorithm (FAFA), in which an adaptive parameter strategy based on Lévy flight is used to improve the performance of the standard firefly algorithm (FA). The performance of the proposed algorithm has been evaluated using several benchmark images and has been compared with five different heuristic algorithms. The experimental results showed that the proposed algorithm prevailed over the other five competitive algorithms in terms of the objective function value, image quality measures, and computational efficiency.

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Acknowledgments

This work was supported by the GDAS' Project of Building a World-class Research Institution in China under Grant 2020GDASYL-20200402007, GDAS' Project of Science and Technology Development under Grant 2018GDASCX-0115 and 2017GDASCX-0115.

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Authors and Affiliations

  1. College of Computer and Information, City College of Dongguan University of Technology, Dongguan, 523419, Guangdong, China

    Ling Peng

  2. Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, 510000, Guangdong, China

    Dongbo Zhang

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  1. Ling Peng
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  2. Dongbo Zhang
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Correspondence to Dongbo Zhang.

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The original online version of this article was revised: In this article the affiliation details for author Ling Peng were incorrectly given.

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Peng, L., Zhang, D. An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy. J Supercomput 78, 6875–6896 (2022). https://doi.org/10.1007/s11227-021-04150-3

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