IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v493y2018icp177-188.html
   My bibliography  Save this article

Super resolution reconstruction of μ-CT image of rock sample using neighbour embedding algorithm

Author

Listed:
  • Wang, Yuzhu
  • Rahman, Sheik S.
  • Arns, Christoph H.

Abstract

X-ray computed tomography (μ-CT) is considered to be the most effective way to obtain the inner structure of rock sample without destructions. However, its limited resolution hampers its ability to probe sub-micro structures which is critical for flow transportation of rock sample. In this study, we propose an innovative methodology to improve the resolution of μ-CT image using neighbour embedding algorithm where low frequency information is provided by μ-CT image itself while high frequency information is supplemented by high resolution scanning electron microscopy (SEM) image. In order to obtain prior for reconstruction, a large number of image patch pairs contain high- and low- image patches are extracted from the Gaussian image pyramid generated by SEM image. These image patch pairs contain abundant information about tomographic evolution of local porous structures under different resolution spaces. Relying on the assumption of self-similarity of porous structure, this prior information can be used to supervise the reconstruction of high resolution μ-CT image effectively. The experimental results show that the proposed method is able to achieve the state-of-the-art performance.

Suggested Citation

  • Wang, Yuzhu & Rahman, Sheik S. & Arns, Christoph H., 2018. "Super resolution reconstruction of μ-CT image of rock sample using neighbour embedding algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 177-188.
    • Handle: RePEc:eee:phsmap:v:493:y:2018:i:c:p:177-188
    DOI: 10.1016/j.physa.2017.10.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117310324
    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.2017.10.022?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. Latief, F.D.E. & Biswal, B. & Fauzi, U. & Hilfer, R., 2010. "Continuum reconstruction of the pore scale microstructure for Fontainebleau sandstone," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1607-1618.
    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. Liqun Shan & Chengqian Liu & Yanchang Liu & Weifang Kong & Xiali Hei, 2022. "Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network," Energies, MDPI, vol. 15(14), pages 1-18, July.

    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. Chen, Dongdong & He, Xiaohai & Teng, Qizhi & Xu, Zhi & Li, Zhengji, 2014. "Reconstruction of multiphase microstructure based on statistical descriptors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 240-250.
    2. Chen, DongDong & Liu, Yang & Wang, XiaoRui & Wang, Mei, 2022. "Reconstruction of the microstructure based on hierarchical simulated annealing with erosion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Zou, Xiaojing & He, Changyu & Guan, Wei & Zhou, Yan & Zhao, Hongyang & Cai, Mingyu, 2023. "Reservoir tortuosity prediction: Coupling stochastic generation of porous media and machine learning," Energy, Elsevier, vol. 285(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:493:y:2018:i:c:p:177-188. 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.