A Fast Satellite Image Super-Resolution Technique Using Multicore Processing

  • Helal Uddin Mullah
  • Bhabesh DekaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 734)


Sparse representation based single image super-resolution technique requires several regularization problems to be solved to generate the desired output. It is computationally intensive and needs a considerable time if we implement sequentially on a single core processor. Remote sensing applications generally require high resolution satellite images on a near real-time instant. Since, satellite images are of larger dimensions, so obtaining desired high resolution images within some practical time will be highly data intensive. Therefore, fast super-resolution based post-processing may be integrated into the existing system either in software or hardware for practical applications. In this paper, we implement an OpenMP based parallel processing technique for single image super-resolution of multispectral satellite images. Results not only show a promising speed up in the execution time but provide visually enhanced outputs as well, compared to some of the existing methods.


Super-resolution Sparse representation OpenMP Multicore processing Satellite image 



Authors would like to thank Tezpur University, India for providing access to PARAMTEZ supercomputer and ISRO for providing funds under the RESPOND project (sanction No. ISRO/RES/4/642/17-18) which helped in smooth conduction of the work.


  1. 1.
    Kamal, N., Moeslund, T.B.: Super-resolution: a comprehensive survey. Mach. Vis. Appl. 25(6), 1423–1468 (2014)CrossRefGoogle Scholar
  2. 2.
    Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Sig. Process. Mag. 20(3), 21–36 (2003)CrossRefGoogle Scholar
  3. 3.
    Tsai, R.Y., Huang, T.S.: Multiframe image restoration and registration. Adv. Comput. Vis. Image Process. 1(2), 317–339 (1984)Google Scholar
  4. 4.
    Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D. et al. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: Proceedings of IEEE 12th International Conference on Computer Vision, pp. 349–356 (2009)Google Scholar
  6. 6.
    Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 275–282 (2004)Google Scholar
  7. 7.
    Freeman, W.T., Jones, T.R., Pasztor, E.C.: Example based super-resolution. IEEE J. Comput. Graph. Appl. 22(2), 56–65 (2002)CrossRefGoogle Scholar
  8. 8.
    Deka, B., Gorain, K.K., Kalita, N., Das, B.: Single image super-resolution using compressive sensing with learned overcomplete dictionary. In: Proceedings of IEEE National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, pp. 1–5 (2013)Google Scholar
  9. 9.
    Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ramos, V.A., Ponomaryov, V., Rogelio, R.R., Francisco, G.F.: Satellite image super-resolution using overlapping blocks via sparse representation. In: Proceedings of IEEE 9th International Symposium of Physics and Engineering of Microwaves, Millimeter and Submillimeter Waves, pp. 1–4 (2016)Google Scholar
  11. 11.
    Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001)CrossRefGoogle Scholar
  12. 12.
    Zhao, Y., Yang, J., Zhang, Q., Song, L., Cheng, Y., Pan, Q.: Hyperspectral imagery super-resolution by sparse representation and spectral regularization. EURASIP J. Adv. Sig. Process. 2011(1), 1–10 (2011)CrossRefGoogle Scholar
  13. 13.

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringTezpur UniversityTezpurIndia

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