## Zhonghua Wang* and Faliang Huang**## |

Sobel | Laplacian | Canny | Kefu | Our method | |

P_{l} | 0.549 | 0.490 | 0.579 | 0.539 | 0.795 |

GSSIM | 0.563 | 0.555 | 0.661 | 0.610 | 0.804 |

Table 2.

Sobel | Laplacian | Canny | Kefu | Our method | |

P_{l} | 0.708 | 0.411 | 0.579 | 0.905 | 0.936 |

GSSIM | 0.705 | 0.446 | 0.661 | 0.816 | 0.831 |

As seen in Tables 1 and 2, the *P _{l}* and GSSIM by adopting our algorithm are larger than that of other operators, indicating that our model can preserve more edge details and suppress more pseudo edges. For the defect image detection, though our method slightly prolongs the running time, we are more concerned with the edge detection accuracy rather than the running time.

The edge detection is the base of image segmentation, image understanding and recognition. In order to improve the edge detection precision in defect image, according to the pixel’s gray scale saliency, edge statistical characteristics and gray scale contrast in defect image, a triqubit-state measurementbased edge detection algorithm is proposed. Firstly, the improved PM model is used to smooth the defect image. Secondly, the triqubit space is constructed by the image edge regularity. Thirdly, the edge image is obtained by the quantum measurement, local gradient maximization and 8-neighborhood chain code searching. In the light of the objective evaluations of the image quality factor and global structural similarity index or the subjective visual evaluation, our algorithm can preserve more image edge details and suppress more noise.

The work is supported by the Natural Science Foundation of Province of China (No. 20132BAB201024 and 20161BAB202037), the Key Laboratory of Non-destructive Testing of Education Ministry of China (No. ZD201429005), the Science and Technology Research Project of Jiangxi Province Education Department of China (No. GJJ160696), the Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing of China (No. 2016WICSIP007), and China Post-Doctoral Science Foundation (No. 2017M622108).

He was born in China in 1977. He received the Ph.D. degree in Control Science and Engineering from Huazhong University of Science and Technology, China, in 2011. He is currently an associate professor of Nanchang Hangkong University, China. His research interests include the image processing, pattern recognition and artificial intelligence. He has hosted or attended several National Natural Science Fund Projects of China. Triqubit-State Measurement-Based Image Edge Detection Algorithm

He was born in China in 1987. He received the Master degree in Electronic and Communication Engineering from Nanchang Hangkong University, China, in 2016. He is the engineer of Beijing Xinyihua Technology Co., Ltd. His research interests include the image processing and pattern recognition. He has attended several National Natural Science Fund Projects of China.

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