IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v605y2022ics0378437122006379.html
   My bibliography  Save this article

Quantum SUSAN edge detection based on double chains quantum genetic algorithm

Author

Listed:
  • Wu, Chenyi
  • Huang, Fei
  • Dai, Jingyi
  • Zhou, Nanrun

Abstract

Edge detection algorithm based on quantum image processing has attracted much attention due to low circuit complexity and small storage capacity. Since the classical smallest univalue segment assimilating nucleus (SUSAN) algorithm is limited in vertical and horizontal directions, a new quantum SUSAN edge detection scheme based on double chains quantum genetic algorithm is designed. First, the linear X-shift and the Y-shift transforms are raised to accelerate the qubit retrieval process and output the quantum feature information. Then, the quantum feature information is fed to quantum comparator and surround suppression circuit exports gradient information. Finally, the quantum SUSAN classifier outputs classification results. The presented algorithm combines the advantages of SUSAN’s accurate positioning and texture edge suppression with the superposition of the quantum genetic algorithm, which avoids the trap of local optimum. In the quantum SUSAN classifier circuit, the double chains quantum genetic coding and the circular SUSAN mask are introduced to improve classification accuracy. Experimental results verify that the proposed scheme has a good edge searchability. Moreover, the edge points obtained by this algorithm are more integral and continuous than other classical algorithms and existing quantum algorithms.

Suggested Citation

  • Wu, Chenyi & Huang, Fei & Dai, Jingyi & Zhou, Nanrun, 2022. "Quantum SUSAN edge detection based on double chains quantum genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
  • Handle: RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006379
    DOI: 10.1016/j.physa.2022.128017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122006379
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2022.128017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gong, Li-Hua & Xiang, Ling-Zhi & Liu, Si-Hang & Zhou, Nan-Run, 2022. "Born machine model based on matrix product state quantum circuit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    2. Sheng Jiang & Guoan Tang & Kai Liu, 2015. "A New Extraction Method of Loess Shoulder-Line Based on Marr-Hildreth Operator and Terrain Mask," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zeng, Qing-Wei & Ge, Hong-Ying & Gong, Chen & Zhou, Nan-Run, 2023. "Conditional quantum circuit Born machine based on a hybrid quantum–classical​ framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeng, Qing-Wei & Ge, Hong-Ying & Gong, Chen & Zhou, Nan-Run, 2023. "Conditional quantum circuit Born machine based on a hybrid quantum–classical​ framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:605:y:2022:i:c:s0378437122006379. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.