IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5115-d862022.html
   My bibliography  Save this article

Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Author

Listed:
  • Liqun Shan

    (School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing 163318, China
    School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA)

  • Chengqian Liu

    (School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing 163318, China)

  • Yanchang Liu

    (School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing 163318, China)

  • Weifang Kong

    (School of Physical and Electrical Engineering, Northeast Petroleum University, Daqing 163318, China)

  • Xiali Hei

    (School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA 70503, USA)

Abstract

Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5115-:d:862022
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5115/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/14/5115/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. Mingyue Jia & Wenhui Huang & Yuan Li, 2023. "Quantitative Characterization of Pore Structure Parameters in Coal Based on Image Processing and SEM Technology," Energies, MDPI, vol. 16(4), pages 1-19, February.

    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.

      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:gam:jeners:v:15:y:2022:i:14:p:5115-:d:862022. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      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.