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Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation

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Abstract

Kernel fuzzy weighted local information C-means clustering is a widely used robust segmentation algorithm for noisy images. However, it cannot effectively solve the segmentation problem of blurred and noisy images. A local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation is proposed in this paper. Firstly, a local ternary pattern is used to extract the feature information of the blurred and noisy images; Secondly, the image feature information is embedded into the objective function of fuzzy local information clustering and an optimization model for blurred and noisy image segmentation is established. Thirdly, Lagrange multiplier method is used to solve this optimization problem, and a dual-level alternating iterative clustering algorithm for blurred and noisy image segmentation is obtained. Experimental results demonstrate that the proposed algorithm has better segmentation performance for blurred and noisy images than the latest robust fuzzy clustering-related algorithms, and its PSNR and ACC values increase by about 0.09 ~ 1.07 and 0.08 ~ 0.13, respectively.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers 61671377), and the Natural Science Foundation of Shaanxi Province (2022JM-370). Wu and Qi would like to thank the anonymous reviewers for their constructive suggestions to improve the overall quality of the paper. Besides, Wu and Qi would like to thank the School of Electronic Engineering, Xi’an University of Posts & Telecommunications, Xi’an, China for financial support.

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  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, People’s Republic of China

    Chengmao Wu & Xiao Qi

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  1. Chengmao Wu
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ChengmaoWu: Conceptualization, Writing-review and editing. Xiao Qi: Software, Methodology, Writing-originaldraft, Writing-reviewa and editing.

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Correspondence to Xiao Qi.

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Wu, C., Qi, X. Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation. J Real-Time Image Proc 20, 116 (2023). https://doi.org/10.1007/s11554-023-01371-y

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