Quantum image edge extraction based on Laplacian operator and zero-cross method

  • Ping Fan
  • Ri-Gui ZhouEmail author
  • Wen Wen Hu
  • NaiHuan Jing


Edge detection, as a fundamental problem in image processing and computer vision, is an indispensable task in digital image processing. Because of the sharp increase in the image data in the actual applications, real-time problem has become a limitation in classical image processing. In this paper, based on the novel enhanced quantum image representation (NEQR) of digital images, an enhanced quantum edge detection algorithm is investigated, which combines the classical Laplacian operator and zero-cross method. Because NEQR utilizes the superposition state of qubit sequence to store all the pixels of an image, the corresponding quantum image edge detection algorithm can realize parallel computation to implement the Laplacian filter and further calculate the image intensity of all the pixels according zero-cross method. The circuit complexity analysis demonstrates that our presented quantum image edge algorithm can reach a significant and exponential speedup compared to classical counterparts. Hence, our proposed quantum image edge detection algorithm would resolve the real-time problem of image edge extraction in practice image processing.


Quantum image processing Edge detection Laplacian operator Zero-cross method 



This work is supported by the National Natural Science Foundation of China under Grant Nos. 61763014, 61463016, 61462026, and 61762012, the National Key R&D Plan under Grant No. 2018YFC1200200 and 2018YFC1200205, the Fund for Distinguished Young Scholars of Jiangxi Province under Grant No. 2018ACB21013, Science and technology research project of Jiangxi Provincial Education Department under Grant No. GJJ170382, Project of International Cooperation and Exchanges of Jiangxi Province under Grant No. 20161BBH80034, Project of Humanities and Social Sciences in colleges and universities of Jiangxi Province under Grant No. JC161023.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ping Fan
    • 1
  • Ri-Gui Zhou
    • 2
    Email author
  • Wen Wen Hu
    • 2
  • NaiHuan Jing
    • 3
  1. 1.School of Information EngineeringEast China Jiaotong UniversityNanchangChina
  2. 2.College of Information EngineeringShanghai Maritime UniversityShanghaiChina
  3. 3.Department of MathematicsNorth Carolina State UniversityRaleighUSA

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