Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator
Aiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/9594301 |
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| _version_ | 1832556374558507008 |
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| author | Chunsheng Liu Chunping Ren |
| author_facet | Chunsheng Liu Chunping Ren |
| author_sort | Chunsheng Liu |
| collection | DOAJ |
| description | Aiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted to reconstruct the input coal-rock infrared images, so as to reduce the noise interference, and then, the reconstructed image is transformed by gray level. Finally, we consider the frequency characteristics and long memory properties of fractional differential, the classical first-order Sobel and second-order Laplacian edge detection algorithms are extended to fractional order pattern, and a new pattern of fractional order differential image edge detection is constructed to realize the coal-rock fracture edge features identification. The results show that, compared with integer order differential, the error rate and omission rate of fractional order differential algorithm are smaller, the quality factor is larger, and the execution time and memory footprint are smaller. From the point of view of location criteria and location accuracy, the fractional order differential algorithm is better than the integer order. In addition, the proposed method is compared with Canny algorithm, B-spline wavelet transform, and multidirection fuzzy morphological edge detection method, can detect more coal-rock fracture infrared image edge details, and is more robust to noise. |
| format | Article |
| id | doaj-art-fa02e86f96964829ae2d8c2cf4f3ff4b |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-fa02e86f96964829ae2d8c2cf4f3ff4b2025-02-03T05:45:46ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552019-01-01201910.1155/2019/95943019594301Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential OperatorChunsheng Liu0Chunping Ren1Heilongjiang University of Science and Technology, Harbin 150022, ChinaHeilongjiang University of Science and Technology, Harbin 150022, ChinaAiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted to reconstruct the input coal-rock infrared images, so as to reduce the noise interference, and then, the reconstructed image is transformed by gray level. Finally, we consider the frequency characteristics and long memory properties of fractional differential, the classical first-order Sobel and second-order Laplacian edge detection algorithms are extended to fractional order pattern, and a new pattern of fractional order differential image edge detection is constructed to realize the coal-rock fracture edge features identification. The results show that, compared with integer order differential, the error rate and omission rate of fractional order differential algorithm are smaller, the quality factor is larger, and the execution time and memory footprint are smaller. From the point of view of location criteria and location accuracy, the fractional order differential algorithm is better than the integer order. In addition, the proposed method is compared with Canny algorithm, B-spline wavelet transform, and multidirection fuzzy morphological edge detection method, can detect more coal-rock fracture infrared image edge details, and is more robust to noise.http://dx.doi.org/10.1155/2019/9594301 |
| spellingShingle | Chunsheng Liu Chunping Ren Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator Journal of Electrical and Computer Engineering |
| title | Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator |
| title_full | Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator |
| title_fullStr | Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator |
| title_full_unstemmed | Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator |
| title_short | Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator |
| title_sort | research on coal rock fracture image edge detection based on tikhonov regularization and fractional order differential operator |
| url | http://dx.doi.org/10.1155/2019/9594301 |
| work_keys_str_mv | AT chunshengliu researchoncoalrockfractureimageedgedetectionbasedontikhonovregularizationandfractionalorderdifferentialoperator AT chunpingren researchoncoalrockfractureimageedgedetectionbasedontikhonovregularizationandfractionalorderdifferentialoperator |