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Sharpness evaluation algorithm for nailfold microvascular images

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Abstract

Observing and analyzing nailfold microvascular images are essential for evaluating human health, but effective analysis requires high-clarity images. Autofocus technology enables the efficient and convenient acquisition of high-quality images for clinical examinations. However, successful autofocus depends on the accuracy of the sharpness evaluation algorithm. Owing to the low contrast of nailfold microvascular images and the inevitable dithering artifacts and stray light produced during acquisition, existing algorithms cannot accurately evaluate sharpness, leading to autofocus failure. To address this problem, this study proposes a sharpness evaluation algorithm that uses histogram equalization to enhance the high-frequency information of the images, applies wavelet transform to reduce the influence of dithering artifacts and stray light, and calculates the edge gradient to evaluate sharpness quantitatively. Five mainstream evaluation algorithms were compared to validate the performance of the proposed algorithm, and the results show that it outperforms the others and accurately determines the clearest image. Compared with other algorithms, the clearest image exhibits the best visual effect, with a narrow width of the evaluation curve that is 27–37% higher. The algorithm proposed in this article has better real-time applicability.

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Data Availability

The basic data of this article will be shared with corresponding authors upon reasonable request.

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Funding

This work was supported by Guangdong Provincal Key Field R&D Plan Project (NO. 2020B1111120004, the National Natural Science Foundation of China under grant no. 62075042, the Science and Technology Program of Guangdong Province under grant no. X190311UZ190, and the 2022 Academic Fund of Foshan University.

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Foshan University, Foshan, 528000, China

    An Huang

  2. School of Physics and Optoelectronic Engineering, Foshan University, Foshan, 528000, China

    Zhiwei Wu, Hao Yin, Qianyao Ye, Junzhao Liang, Jianan Lin, Mugui Xie, Cong Ye, Xiaosong Li & Yanxiong Wu

  3. Ji Hua Laboratory, Foshan, 528200, Guangdong, China

    Yanxiong Wu

Authors
  1. An Huang
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  2. Zhiwei Wu
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  3. Hao Yin
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  4. Qianyao Ye
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  5. Junzhao Liang
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  8. Cong Ye
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  9. Xiaosong Li
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  10. Yanxiong Wu
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Contributions

AH proposed this idea and mainly worked on the implementation, algorithm development, and testing. ZW reviewed the manuscript and made suggestions. HY and QY assisted in algorithm development. JLiang and JLin collected the required images for the experiment. MX and CY participated in image acquisition work. YW and XL have made contributions to general work supervision and sought funding to support research work. All authors contributed to the manuscript writing and review.

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Correspondence to Yanxiong Wu.

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Huang, A., Wu, Z., Yin, H. et al. Sharpness evaluation algorithm for nailfold microvascular images. SIViP 18, 1515–1523 (2024). https://doi.org/10.1007/s11760-023-02873-9

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  • DOI: https://doi.org/10.1007/s11760-023-02873-9

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