Skip to main content
Log in

Style Transfer applied to CT image downscaling: a study case from Brazilian Coquinas

  • Original Paper
  • Published:
Computational Geosciences Aims and scope Submit manuscript

Abstract

The identification of micropore systems in carbonate rocks is an important task of image processing because of the high impact these systems cause on fluid flow. Currently, one of the main tools used to characterize rock samples is computed tomography (CT). Such micro information poses the challenge associated with the limitation of the CT’s resolution. Therefore, we propose an alternative method of inserting the micropore features of μ CT to lower resolution images, but with higher coverage. We can perform this by a novel application of Style Transfer that can insert the heterogeneity pattern of high-resolution (HR) images (CT of 7 and 40 μm resolution) into low-resolution (LR) images (CT of 90 μm resolution), downscaling the image through a super-resolution method. This technique uses the power of VGG19, a convolutional neural network that won the ImageNet Large-Scale Visual Recognition Challenge in 2014, as a texture extractor. We applied this novel technique to condensed shell rocks, called coquinas, from the Itapema Formation, Santos Basin offshore Brazil. The porosity of the LR image, with initial average value of 11%, resulted in an average porosity of 12% (40 μm res.) and 13% (7 μm res.) after downscaling. This is closer to the porosity range of the coquina (13% to 32%, with a mean of 21%) and an increase in the porosity of 6% and 19% in average, respectively. Despite this, the connectivity of the original LR CT was of 3% on average and, in the simulated HR CT, the connectivity was of 5% (40 μm res.) and 6% (7 μm res.). In addition, in such examples, this method inserted connectivity in directions that were null in low-resolution images before the style transfer. Hence, the results demonstrated that the Style Transfer offers an alternative for downscaling CT images by inserting the texture from high-resolution images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. MagicaVoxel repository: https://ephtracy.github.io/

References

  1. Abdal, R., Qin, Y., Wonka, P.: Image2StyleGAN++: How to edit the embedded images?. arXiv:1911.11544 (2019)

  2. Agarap, A.F.: Deep learning using rectified linear units (ReLU). arXiv:1803.08375 (2018)

  3. Arns, C.H., Bauget, F., Limaye, A., Sakellariou, A., Senden, T., Sheppard, A., Sok, R.M., Pinczewski, V., Bakke, S., Berge, L.I., Oren, P.E., Knackstedt, M.A.: Pore scale characterization of carbonates using x-ray microtomography, vol. 10. https://doi.org/10.2118/90368-PA. http://www.onepetro.org/doi/10.2118/90368-PA (2005)

  4. Bauer, D., Youssef, S., Fleury, M., Bekri, S., Rosenberg, E., Vizika, O.: Improving the estimations of petrophysical transport behavior of carbonate rocks using a dual pore network approach combined with computed microtomography. Transp. Porous Media 94(2), 505–524 (2012). https://doi.org/10.1007/s11242-012-9941-z

    Article  Google Scholar 

  5. Bhalley, R., Su, J.: Artist style transfer via quadratic potential. arXiv:1902.11108 (2019)

  6. Boone, M.A., De Kock T, Bultreys, T., De Schutter. G., Vontobel, P., Van Hoorebeke. L., Cnudde, V.: 3D mapping of water in oolithic limestone at atmospheric and vacuum saturation using X-ray micro-CT differential imaging, vol. 97. https://doi.org/10.1016/j.matchar.2014.09.010 (2014)

  7. Bultreys, T., Stappen, J.V., Kock, T.D., Boever, W.D., Boone, M.A., Hoorebeke, L.V., Cnudde, V.: Investigating the relative permeability behavior of microporosity-rich carbonates and tight sandstones with multiscale pore network models. J. Geophys. Res. Solid Earth 121(11), 7929–7945 (2016). https://doi.org/10.1002/2016JB013328

    Article  Google Scholar 

  8. Chen, X., Xu, C., Yang, X., Song, L., Tao, D.: Gated-GAN: Adversarial Gated Networks for Multi-Collection Style Transfer. IEEE Trans Image Process 28(2), 546–560 (2019). https://doi.org/10.1109/TIP.2018.2869695. arXiv:1904.02296

    Article  Google Scholar 

  9. Chi, D.: Self-organizing map-based color image segmentation with k-means clustering and saliency map. ISRN Signal Process, 2011(1). https://doi.org/10.5402/2011/393891 (2011)

  10. Chinelatto, G.F., Belila, A.M.P., Basso, M., Souza, J.P.P., Vidal, A.C.: A taphofacies interpretation of shell concentrations and their relationship with petrophysics: A case study of Barremian-Aptian coquinas in the Itapema Formation, Santos Basin-Brazil. Mar. Pet. Geol. 116, 104317 (2020). https://doi.org/10.1016/j.marpetgeo.2020.104317. https://linkinghub.elsevier.com/retrieve/pii/S0264817220301008

    Article  Google Scholar 

  11. Cnudde, V., Boone, M.: High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Sci. Rev. 123, 1–17 (2013). https://doi.org/10.1016/J.EARSCIREV.2013.04.003. https://www.sciencedirect.com/science/article/pii/S001282521300069X

    Article  Google Scholar 

  12. Cristóbal, G., Gil, E., CSroubek, F., Flusser, J., Miravet, C., Rodríguez, FB: Superresolution imaging: a survey of current techniques. pp. 70740C. https://doi.org/10.1117/12.797302, http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.797302 (2008)

  13. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.-F.: ImageNet: A large-scale hierarchical image database. In: 2009 ieee conference on computer vision and pattern recognition, pp. 248–255. IEEE. https://doi.org/10.1109/CVPR.2009.5206848. http://ieeexplore.ieee.org/document/5206848/ (2009)

  14. Derluyn, H., Dewanckele, J., Boone, M.N., Cnudde, V., Derome, D., Carmeliet, J.: Crystallization of hydrated and anhydrous salts in porous limestone resolved by synchrotron X-ray microtomography. Nucl. Instrum. Methods Phys. Res. B 324, 102–112 (2014). https://doi.org/10.1016/j.nimb.2013.08.065

    Article  Google Scholar 

  15. Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Graph. 30(2), 1–11 (2011). https://doi.org/10.1145/1944846.1944852. http://portal.acm.org/citation.cfm?doid=1944846.1944852

    Article  Google Scholar 

  16. Gao, W., Li, Y., Yin, Y., Yang, M.H.: Fast video multi-style transfer. In: Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, Institute of Electrical and Electronics Engineers Inc., pp 3211–3219 (2020), https://doi.org/10.1109/WACV45572.2020.9093420

  17. Gatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv:1508.06576 (2015)

  18. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 2414–2423. IEEE Computer Society (2016). https://doi.org/10.1109/CVPR.2016.265

  19. Herring, A.L., Robins, V., Sheppard, A.P.: Topological persistence for relating microstructure and capillary fluid trapping in sandstones. Water Resour. Res. 55(1), 555–573 (2019). https://doi.org/10.1029/2018WR022780

    Article  Google Scholar 

  20. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., vol. 2017-Octob, pp. 1510–1519. https://doi.org/10.1109/ICCV.2017.167, arXiv:1703.06868 (2017)

  21. Jagucki, M.L., Darner, R.A.: Ground-Water Quality in Geauga County, Ohio—Review of Previous Studies, Status in 1999, and Comparison of 1986 and 1999 Data. Tech. rep. https://doi.org/10.3133/wri014160, https://pubs.er.usgs.gov/publication/wri014160 (2001)

  22. Yu, J., Gao, X., Tao, D., Li, X., Zhang, K.: A unified learning framework for single image super-resolution. IEEE Trans. Neural Netw. Learn. Syst. 25(4), 780–792 (2014). https://doi.org/10.1109/TNNLS.2013.2281313. http://ieeexplore.ieee.org/document/6671477/

    Article  Google Scholar 

  23. Jing, Z., Guang-Xun, D., Quan, Q.: Initial research on vibration reduction for quadcopter attitude control: An additive-state-decomposition-based dynamic inversion method. In: Proceedings - 2017 Chinese Automation Congress, CAC 2017 2017-Janua:1–6. https://doi.org/10.1109/CAC.2017.8242726. arXiv:1705.04058 (2017)

  24. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9906 LNCS, pp. 694–711. Springer. https://doi.org/10.1007/978-3-319-46475-6_43 (2016)

  25. Klein, E., Reuschlé, T.: A model for the mechanical behaviour of bentheim sandstone in the brittle regime. In: Thermo-Hydro-Mechanical Coupling in Fractured Rock, pp 833–849. Birkhäuser, Basel (2003). https://doi.org/10.1007/978-3-0348-8083-1_3

  26. Kohonen, T., Schroeder, M.R., Huang, T.S.: Self-organizing maps. Springer, Berlin (2001). https://dl.acm.org/citation.cfm?id=558021

    Book  Google Scholar 

  27. Kolkin, N., Salavon, J., Shakhnarovich, G.: Style transfer by relaxed optimal transport and self-similarity. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019-June, pp. 10043–10052. IEEE Computer Society. https://doi.org/10.1109/CVPR.2019.01029, arXiv:1904.12785 (2019)

  28. Kovalenko, B.: Super resolution with generative adversarial networks. Tech. rep.. http://cs231n.stanford.edu/reports/2017/pdfs/17.pdf (2017)

  29. Kurzman, L., Vazquez, D., Laradji, I.: Class-based styling: Real-time localized style transfer with semantic segmentation. In: Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, pp. 3189–3192, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICCVW.2019.00396. arXiv:1908.11525 (2019)

  30. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015). https://doi.org/10.1038/nature14539, http://www.nature.com/articles/nature14539, arXiv:1312.6184v5

    Article  Google Scholar 

  31. Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. arXiv:1609.04802 (2016)

  32. Li, M., Ye, C., Li, W.: High-resolution network for photorealistic style transfer. In: JMLR: Workshop and Conference Proceedings. arXiv:1904.11617, vol. 101, pp 1–14 (2019)

  33. Li, Y., Liu, M.Y., Li, X., Yang, M.H., Kautz, J.: A closed-form solution to photorealistic image stylization. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11207 LNCS, pp. 468–483. Springer. https://doi.org/10.1007/978-3-030-01219-9_28, arXiv:1802.06474 (2018)

  34. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982). https://doi.org/10.1109/TIT.1982.1056489. http://ieeexplore.ieee.org/document/1056489/

    Article  Google Scholar 

  35. Lu, M., Zhao, H., Yao, A., Chen, Y., Xu, F., Zhang, L.: A closed-form solution to universal style transfer. In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., vol. 2019-Octob, pp. 5951–5960 . https://doi.org/10.1109/ICCV.2019.00605. arXiv:1906.00668 (2019)

  36. Mordensky, S.P., Rabjohns, K., Harris, A., Lieuallen, A.E., Verba, C.: Characterization of the Oriskany and Berea Sandstones: Evaluating Biogeochemical Reactions of Potential Sandstone–Hydraulic Fracturing Fluid Interaction. Tech. rep., National Energy Technology Laboratory. https://netl.doe.gov/projects/files/CharacterizationoftheOriskanyandBereaSandstonesEvaluatingBiogeochemicalReactions_112216.pdf (2016)

  37. Moreira, J., Costa, L.d.F.: Neural-based color image segmentation and classification using self-organizing maps. Anais do IX SIBGRAPI, 47–54 (1996)

  38. Mroueh, Y.: Wasserstein Style Transfer. arXiv:1905.12828 (2019)

  39. Nasrollahi, K., Moeslund, T.B.: Super-resolution: a comprehensive survey. Mach. Vis. Appl. 25(6), 1423–1468 (2014). https://doi.org/10.1007/s00138-014-0623-4

    Article  Google Scholar 

  40. van Ouwerkerk, J.: Image super-resolution survey. Image Vis. Comput. 24 (10), 1039–1052 (2006). https://doi.org/10.1016/J.IMAVIS.2006.02.026, https://www.sciencedirect.com/science/article/pii/S0262885606001089

    Article  Google Scholar 

  41. Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in PyTorch. In: NIPS-W. https://openreview.net/pdf?id=BJJsrmfCZ (2017)

  42. Peksa, A.E., Wolf, K.H.A., Zitha, P.L.: Bentheimer sandstone revisited for experimental purposes. https://doi.org/10.1016/j.marpetgeo.2015.06.001 (2015)

  43. Ristić, D.M., Pavlović, M., Reljin, I.: Image segmentation method based on self-organizing maps and K-means algorithm. In: 9th Symposium on Neural Network Applications in Electrical Engineering, NEUREL 2008 Proceedings, pp. 27–30. https://doi.org/10.1109/NEUREL.2008.4685551 (2008)

  44. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. Tech. rep. http://image-net.org/challenges/LSVRC/, arXiv:1409.0575v3 (2015)

  45. Salazar, M.O., Villa Piamo, J.R.: Permeability upscaling techniques for reservoir simulation. In: Latin American & Caribbean Petroleum Engineering Conference, Society of Petroleum Engineers. https://doi.org/10.2118/106679-MS (2007)

  46. Schulter, S., Leistner, C., Bischof, H.: Fast and accurate image upscaling with super-resolution forests. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 3791–3799. IEEE. https://doi.org/10.1109/CVPR.2015.7299003. http://ieeexplore.ieee.org/document/7299003/ (2015)

  47. Sheng, L., Lin, Z., Shao, J., Wang, X.: Avatar-Net: multi-scale zero-shot style transfer by feature decoration. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 8242–8250. IEEE Computer Society. https://doi.org/10.1109/CVPR.2018.00860, arXiv:1805.03857 (2018)

  48. Shrestha, A., Mahmood, A.: Review of deep learning algorithms and architectures. https://doi.org/10.1109/ACCESS.2019.2912200 (2019)

  49. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 (2014)

  50. Song, C., Wu, Z., Zhou, Y., Gong, M., Huang, H.: ETNet: Error Transition Network for Arbitrary Style Transfer. arXiv:1910.12056 (2019)

  51. Wang, Y.D., Armstrong, R.T., Mostaghimi, P.: Boosting resolution and recovering texture of 2D and 3D Micro-CT images with deep learning. Water Resour. Res., 56(1). https://doi.org/10.1029/2019WR026052 (2020)

  52. Wang, Z., Zhao, L., Chen, H., Qiu, L., Mo, Q., Lin, S., Xing, W., Lu, D.: Diversified Arbitrary Style Transfer via Deep Feature Perturbation. arXiv:1909.08223 (2019)

  53. Yao, J., Wang, C., Yang, Y., Wang, X.: Upscaling of carbonate rocks from micropore scale to core scale. Int. J. Multiscale Comput. Eng. 11(5), 497–504 (2013). https://doi.org/10.1615/IntJMultCompEng.2013005960. http://www.dl.begellhouse.com/journals/61fd1b191cf7e96f,651485ff3727862e,608cfdff25c9f440.html

    Article  Google Scholar 

  54. Yeh, M.C., Tang, S., Bhattad, A., Zou, C., Forsyth, D.: Improving style transfer with calibrated metrics. In: Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020, Institute of Electrical and Electronics Engineers Inc., pp. 3149–3157 . https://doi.org/10.1109/WACV45572.2020.9093351. arXiv:1910.09447 (2020)

  55. Yoo, J., Uh, Y., Chun, S., Kang, B., Ha, J.W.: Photorealistic style transfer via wavelet transforms. In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., vol. 2019-Octob, pp. 9035–9044 . https://doi.org/10.1109/ICCV.2019.00913, arXiv:1903.09760 (2019)

  56. Zeng, K., Yu, J., Wang, R., Li, C., Tao, D.: Coupled deep autoencoder for single image super-resolution. IEEE Trans. Cybern. 47(1), 27–37 (2017). https://doi.org/10.1109/TCYB.2015.2501373, http://ieeexplore.ieee.org/document/7339460/

    Article  Google Scholar 

  57. Zhang, Y., Fang, C., Wang, Y., Wang, Z., Lin, Z., Fu, Y., Yang, J.: Multimodal style transfer via graph cuts. In: Proceedings of the IEEE International Conference on Computer Vision, Institute of Electrical and Electronics Engineers Inc., vol. 2019-Octob, pp. 5942–5950. https://doi.org/10.1109/ICCV.2019.00604, arXiv:1904.04443 (2019)

  58. Zhang, Z., Wang, Z., Lin, Z., Qi, H.: Reference-conditioned super-resolution by neural texture transfer. arXiv:1804.03360 (2018)

  59. Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A comprehensive survey on transfer learning. arXiv:1911.02685 (2019)

Download references

Acknowledgements

The authors would like to acknowledge Equinor for their financial support and for granting permission to publish this study, CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil - Finance Code 001) for their financial support and ANP (Agência Nacional de Petróleo) for providing the database. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research, Thermo Fisher Scientific for providing PerGeos, used in this study, and ephtracyFootnote 1 for providing the MagicaVoxel package used for the 3D pictures.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to João Paulo da Ponte Souza.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Grant from Equinor and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil - Finance Code 001)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Souza, J.P.d.P., Avansi, M.C.K., Belila, A.M.P. et al. Style Transfer applied to CT image downscaling: a study case from Brazilian Coquinas. Comput Geosci 25, 1457–1471 (2021). https://doi.org/10.1007/s10596-021-10055-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10596-021-10055-0

Keywords

Navigation