Optics and Precision Engineering, Volume. 30, Issue 3, 350(2022)

Adaptive Canny operator edge detection under strong noise

Yuhan LIU, He YAN*, Zaozao CHEN, Xiaotang WANG, and Junbin HUANG
Author Affiliations
  • Liangjiang College of Artificial Intelligence, Chongqing University of Technology, Chongqing401147, China
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    The traditional Canny operator cannot effectively filter out the salt and pepper noise generated during the decoding process and transmission of an image, and cannot retain the edge details. To overcome this, an improved Canny operator image edge detection algorithm for operation under strong noise was proposed. According to the extreme value and gray difference of salt and pepper noise, the pixel points were divided into noise points and suspected noise points. The size and weight of the filter window were adaptively changed according to the pixel points after classification, which could reduce the influence of noise while retaining the image details. Then, the Sobel operators for eight directional templates were introduced to calculate the gradient amplitude to improve the edge positioning effect after filtering. Finally, iterative adaptive threshold algorithm and Otsu algorithm were used to select the best threshold to achieve adaptive threshold setting and improve the edge connection effect. The results of the comparative experiment show that after denoising the noisy image, the structural similarity is 0.949, the peak signal-to-noise ratio is increased by 10.97 dB compared with the traditional algorithm, the average edge evaluation is increased by 27.2%, and the F1 value is increased by 34.6%. The proposed algorithm retains the excellent performance of the Canny operator, can effectively remove salt and pepper noise, and has better edge detail protection capabilities.

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    Yuhan LIU, He YAN, Zaozao CHEN, Xiaotang WANG, Junbin HUANG. Adaptive Canny operator edge detection under strong noise[J]. Optics and Precision Engineering, 2022, 30(3): 350

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    Paper Information

    Category: Information Sciences

    Received: Jul. 1, 2021

    Accepted: --

    Published Online: Mar. 4, 2022

    The Author Email: He YAN (cqyanhe@163.com)

    DOI:10.37188/OPE.20223003.0350

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