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Laser & Optoelectronics Progress, Volume. 58, Issue 8, 0810021(2021)

Edge Detection of Noisy Images in NSCT Domain Based on Fractional Differentiation

Junxie Chen1 and Yipeng Liao2、*
Author Affiliations
  • 1College of Artificial Intelligence, Yango University, Fuzhou, Fujian 350015, China
  • 2College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350108, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (17)
    Equations (0)
    References (21)
    Cited By (1)
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    Paper Information
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    Figures & Tables(17)
    Tiansi operator

    Fig. 1. Tiansi operator

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    Decomposing process of NSCT

    Fig. 2. Decomposing process of NSCT

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    Flow chart of the proposed method

    Fig. 3. Flow chart of the proposed method

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    Original images for experiment. (a) Lena; (b) cartoon image

    Fig. 4. Original images for experiment. (a) Lena; (b) cartoon image

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    Edge detection results of Lena image. (a) Low frequency; (b) high frequency scale 1; (c) high frequency scale 2; (d) high frequency; (e) fusion result

    Fig. 5. Edge detection results of Lena image. (a) Low frequency; (b) high frequency scale 1; (c) high frequency scale 2; (d) high frequency; (e) fusion result

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    Edge detection results of cartoon image. (a) Low frequency; (b) high frequency scale 1; (c) high frequency scale 2; (d) high frequency; (e) fusion result

    Fig. 6. Edge detection results of cartoon image. (a) Low frequency; (b) high frequency scale 1; (c) high frequency scale 2; (d) high frequency; (e) fusion result

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    Edge extraction results of Lena image. (a) Original image; (b) image with 10% noise; (c) extraction result with 10% noise; (d) image with 50% noise; (e) extraction result with 50% noise

    Fig. 7. Edge extraction results of Lena image. (a) Original image; (b) image with 10% noise; (c) extraction result with 10% noise; (d) image with 50% noise; (e) extraction result with 50% noise

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    Edge extraction results of medical image 1. (a) Original image; (b) image with 10% noise; (c) extraction result with 10% noise; (d) image with 50% noise; (e) extraction result with 50% noise

    Fig. 8. Edge extraction results of medical image 1. (a) Original image; (b) image with 10% noise; (c) extraction result with 10% noise; (d) image with 50% noise; (e) extraction result with 50% noise

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    Edge extraction results of medical image 2. (a) Original image; (b) image with 10% noise; (c) extraction result with 10% noise; (d) image with 50% noise; (e) extraction result with 50% noise

    Fig. 9. Edge extraction results of medical image 2. (a) Original image; (b) image with 10% noise; (c) extraction result with 10% noise; (d) image with 50% noise; (e) extraction result with 50% noise

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    Extracted results of methods for Lena image. (a) Canny; (b) method in Ref. [7]; (c) method in Ref. [15]; (d) method in Ref. [20]; (e) proposed method

    Fig. 10. Extracted results of methods for Lena image. (a) Canny; (b) method in Ref. [7]; (c) method in Ref. [15]; (d) method in Ref. [20]; (e) proposed method

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    Extracted results of methods for cartoon image. (a) Canny; (b) method in Ref. [7]; (c) method in Ref. [15]; (d) method in Ref. [20]; (e) proposed method

    Fig. 11. Extracted results of methods for cartoon image. (a) Canny; (b) method in Ref. [7]; (c) method in Ref. [15]; (d) method in Ref. [20]; (e) proposed method

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    Processing results of different methods for noisy Lena image. (a) Method in Ref. [7]; (b) method in Ref. [15]; (c) method in Ref. [16]; (d) method in Ref. [20]; (e) proposed method

    Fig. 12. Processing results of different methods for noisy Lena image. (a) Method in Ref. [7]; (b) method in Ref. [15]; (c) method in Ref. [16]; (d) method in Ref. [20]; (e) proposed method

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    Processing results of different methods for noisy medical image 1. (a) Method in Ref. [7]; (b) method in Ref. [15]; (c) method in Ref. [16]; (d) method in Ref. [20]; (e) proposed method

    Fig. 13. Processing results of different methods for noisy medical image 1. (a) Method in Ref. [7]; (b) method in Ref. [15]; (c) method in Ref. [16]; (d) method in Ref. [20]; (e) proposed method

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    Processing results of different methods for noisy medical image 2. (a) Method in Ref. [7]; (b) method in Ref. [15]; (c) method in Ref. [16]; (d) method in Ref. [20]; (e) proposed method

    Fig. 14. Processing results of different methods for noisy medical image 2. (a) Method in Ref. [7]; (b) method in Ref. [15]; (c) method in Ref. [16]; (d) method in Ref. [20]; (e) proposed method

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    Comparison of results of different methods for different noise. (a) Lena image; (b) medical image 1; (c) medical image 2

    Fig. 15. Comparison of results of different methods for different noise. (a) Lena image; (b) medical image 1; (c) medical image 2

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    • Table 1. Comparison of extracted results of Lena image by different methods

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      Table 1. Comparison of extracted results of Lena image by different methods

      IndexCannyMethod in Ref. [7]Method in Ref. [15]Method in Ref. [20]Proposed method
      α50641754687295329841
      β6103267880361052910547
      R0.8300.6550.8550.9050.933

    • Table 2. Comparison of extracted results for cartoon image by different methods

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      Table 2. Comparison of extracted results for cartoon image by different methods

      IndexCannyMethod in Ref. [7]Method in Ref. [15]Method in Ref. [20]Proposed method
      α77623378103781321315693
      β92845211120531450116802
      R0.8360.6480.8610.9110.934
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    Junxie Chen, Yipeng Liao. Edge Detection of Noisy Images in NSCT Domain Based on Fractional Differentiation[J]. Laser & Optoelectronics Progress, 2021, 58(8): 0810021

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    Paper Information

    Category: Image Processing

    Received: Jul. 27, 2020

    Accepted: Sep. 24, 2020

    Published Online: Apr. 12, 2021

    The Author Email: Yipeng Liao (fzu_lyp@163.com)

    DOI:10.3788/LOP202158.0810021

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology