Advertisement

Enhancing the Resolution of Satellite Images Using the Best Matching Image Fragment

  • Daniel KostrzewaEmail author
  • Pawel Benecki
  • Lukasz Jenczmyk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11431)

Abstract

Due to very high costs and a long revisit time, it is challenging to obtain good quality satellite images of the area of interest. As a result, super resolution reconstruction (SRR) methods which allow for creating a high-resolution (HR) image based on single or multiple low-resolution (LR) observations are being extensively developed. In this paper, we propose a few improvements to well-known single-image SRR technique based on a dictionary of pairs of matched LR and HR image fragments. The modifications concern both increasing the number of pairs of images fragments and the reconstruction algorithm itself in order to achieve visually pleasing results. This allows us to increase the quality of newly produced HR satellite images what is supported by conducted experiments.

Keywords

Dictionary of matched fragments Image processing Satellite image Single-image super-resolution reconstruction 

Notes

Acknowledgements

This work was supported by research funds of Institute of Informatics, Silesian University of Technology, Gliwice, Poland (grant no. BKM-556/RAU2/2018).

References

  1. 1.
    Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1, p. I. IEEE (2004)Google Scholar
  2. 2.
    Chavez-Roman, H., Ponomaryov, V.: Super resolution image generation using wavelet domain interpolation with edge extraction via a sparse representation. IEEE Geosci. Remote Sens. Lett. 11(10), 1777–1781 (2014)CrossRefGoogle Scholar
  3. 3.
    Demirel, H., Anbarjafari, G.: Image resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans. Image Process. 20(5), 1458–1460 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)CrossRefGoogle Scholar
  5. 5.
    Fattal, R.: Image upsampling via imposed edge statistics. ACM Trans. Graph. (TOG) 26, 95-1–95-8 (2007). Article No. 95Google Scholar
  6. 6.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)CrossRefGoogle Scholar
  7. 7.
    Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 349–356. IEEE (2009)Google Scholar
  8. 8.
    He, L., Qi, H., Zaretzki, R.: Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 345–352. IEEE (2013)Google Scholar
  9. 9.
    Heinrich, L., Bogovic, J.A., Saalfeld, S.: Deep learning for isotropic super-resolution from non-isotropic 3D electron microscopy. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10434, pp. 135–143. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66185-8_16CrossRefGoogle Scholar
  10. 10.
    Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197–5206 (2015)Google Scholar
  11. 11.
    Humm, D., et al.: Flight calibration of the LROC narrow angle camera. Space Sci. Rev. 200(1–4), 431–473 (2016)CrossRefGoogle Scholar
  12. 12.
    Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646–1654 (2016)Google Scholar
  13. 13.
    Kim, K.I., Kwon, Y.: Single-image super-resolution using sparse regression and natural image prior. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1127–1133 (2010)CrossRefGoogle Scholar
  14. 14.
    Kostrzewa, D., Skonieczny, Ł., Benecki, P., Kawulok, M.: B4MultiSR: a benchmark for multiple-image super-resolution reconstruction. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2018. CCIS, vol. 928, pp. 361–375. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-99987-6_28CrossRefGoogle Scholar
  15. 15.
    Li, F., Jia, X., Fraser, D.: Universal HMT based super resolution for remote sensing images. In: 2008 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 333–336. IEEE (2008)Google Scholar
  16. 16.
    Liu, D., Wang, Z., Wen, B., Yang, J., Han, W., Huang, T.S.: Robust single image super-resolution via deep networks with sparse prior. IEEE Trans. Image Process. 25(7), 3194–3207 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Nasrollahi, K., Moeslund, T.B.: Super-resolution: a comprehensive survey. Mach. Vis. Appl. 25(6), 1423–1468 (2014)CrossRefGoogle Scholar
  18. 18.
    Robinson, M., et al.: Lunar reconnaissance orbiter camera (LROC) instrument overview. Space Sci. Rev. 150(1–4), 81–124 (2010)CrossRefGoogle Scholar
  19. 19.
    Shan, Q., Li, Z., Jia, J., Tang, C.K.: Fast image/video upsampling. ACM Trans. Graph. (TOG) 27(5), 153 (2008)CrossRefGoogle Scholar
  20. 20.
    Sheikh, H.R., Bovik, A.C., De Veciana, G.: An information fidelity criterion for image quality assessment using natural scene statistics. IEEE Trans. Image Process. 14(12), 2117–2128 (2005)CrossRefGoogle Scholar
  21. 21.
    SpaceX website. http://www.spacex.com. Accessed 30 Dec 2017
  22. 22.
    Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar
  23. 23.
    Tai, Y.W., Liu, S., Brown, M.S., Lin, S.: Super resolution using edge prior and single image detail synthesis. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2400–2407. IEEE (2010)Google Scholar
  24. 24.
    Wang, L., Huang, Z., Gong, Y., Pan, C.: Ensemble based deep networks for image super-resolution. Pattern Recogn. 68, 191–198 (2017)CrossRefGoogle Scholar
  25. 25.
    Wang, Z., Liu, D., Yang, J., Han, W., Huang, T.: Deep networks for image super-resolution with sparse prior. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 370–378 (2015)Google Scholar
  26. 26.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  27. 27.
    Yue, L., Shen, H., Li, J., Yuan, Q., Zhang, H., Zhang, L.: Image super-resolution: the techniques, applications, and future. Sig. Process. 128, 389–408 (2016)CrossRefGoogle Scholar
  28. 28.
    Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., et al. (eds.) Curves and Surfaces 2010. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-27413-8_47CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of InformaticsSilesian University of TechnologyGliwicePoland

Personalised recommendations