Skip to main content
SpringerLink
Log in

Single-image super-resolution using kernel recursive least squares

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Online single-image super-resolution of an image has been obtained here. The high-resolution image is constructed from a dictionary of features that approximately spans the subspace of regression. This paper classifies the low-resolution image using the kernel k-means clustering algorithm and makes an extensive study using the approximate linear dependence kernel recursive least square and sliding window kernel recursive least squares for super-resolving the image from the existing low- and high-resolution images. The super-resolution using kernel recursive least square significantly provides an improvement up on the support vector regression solution, both in terms of speed, dictionary samples and also gives a better PSNR value.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Allebach, J., Wong, P.W.: Edge-directed interpolation. IEEE Trans. Acoust. Speech. Signal Process. 26(6), 508–517 (1978)

    Article  Google Scholar 

  2. Xin, L., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10(10), 1521–1527 (2001)

    Article  Google Scholar 

  3. Tsai, R. Y., Huang, T. S.: Multiple frame image restoration and registration. In: Advances in Computer Vision and Image Processing. pp. 317–339. JAI Press Inc, Greenwich CT (1984)

  4. Tian, J., Kai-Kuang, M.: A survey on super-resolution imaging. SIViP 5(3), 329342 (2011)

    Article  Google Scholar 

  5. Schultz, R.R., Stevenson, R.L.: Extraction of high-resolution frames from video sequences. IEEE Trans. Image Process. 5(6), 996–1011 (1996)

    Article  Google Scholar 

  6. Hardie, R.C., Barnard, K.J., Armstrong, E.E.: Join MAP registration and high resolution image estimation using a sequence of under sampled images. IEEE Trans. Image Process. 6(12), 1621–1633 (1997)

    Article  Google Scholar 

  7. Stark, H., Oskoui, P.: High-resolution image recovery from image plane arrays, using convex projections. J. Opt. Soc. Am. A 6(11), 1715–1726 (1989)

    Article  Google Scholar 

  8. Tom, B. C., Katsaggelos, A. K., Galatsanos, N. P.: Reconstruction of a high resolution image from registration and restoration of low resolution images. In: Proceedings of IEEE International Conference on IP., pp. 553–557 (1994)

  9. Elad, M., Feuer, A.: Restoration of single super-resolution image from several blurred, noisy and down-sampled measured images. IEEE Trans. Image Process. 6, 1646–1658 (1977)

    Article  Google Scholar 

  10. Capel, D.: Image Mosaicing and Super-Resolution. Springer, Berlin (2004)

    Book  MATH  Google Scholar 

  11. Kaltenbacher, E., Hardie, R. C.: High-resolution infrared image reconstruction using multiple low resolution aliased frames. In: Proceedings of IEEE National Aerospace Electronics Conference, pp. 702–709 (1996)

  12. Capel, D., Zisserman, A.: Computer vision applied to super- resolution. IEEE Signal Process. Mag. 20(3), 75–86 (2003)

    Article  Google Scholar 

  13. Freeman, W.T., Pasztor, E., Carmichael, O.: Learning low-level vision. Int. J. Comput. Vis. 40(1), 25–47 (2000)

    Article  MATH  Google Scholar 

  14. Sun, J., Zheng, N.N., Tao, H., Shum, H.Y.: Image hallucination with primal sketch priors. CVPR 2, 729–736 (2003)

    Google Scholar 

  15. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. CVPR 1, 275–282 (2004)

    Google Scholar 

  16. Fattal, R.: Image upsampling via imposed edge statistics. ACM Trans. Graph. 26(3), 95:1–95:8 (2007)

    Google Scholar 

  17. Ni, K.S., Nguyen, Truong Q.: Image superresolution using support vector regression. IEEE Trans. Image Process. 16(6), 1596–1610 (2007)

    Article  MathSciNet  Google Scholar 

  18. Hofmann, T., Scholkopf, B., Smola, A.J.: Kernel methods in machine learning. Ann. Stat. 36(3), 1171–1220 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  19. Haasdonk, B., Burkhardt, H.: Invariant Kernel Functions for Pattern Analysis and Machine Learning. Springer, Berlin (2007)

    Google Scholar 

  20. Engel, Y., Mannor, S., Meir, R.: The kernel recursive least squares algorithm. IEEE Trans. Signal Proc. 52(8), 2275–2285 (2004)

    Article  MathSciNet  Google Scholar 

  21. Van Vaerenbergh, S., Va, J., Santamara, I.: A sliding-window kernel RLS algorithm and its application to nonlinear channel identification. Proc. IEEE Int. Conf. Acoust. Speech Signal Process. 5, 789–792 (2006)

    Google Scholar 

  22. Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Trans. Neural Netw. 13(3), 780–784 (2002)

    Article  Google Scholar 

  23. Atkins, C. B.: Classification based methods in optimal image interpolation. Ph.D. thesis, Purdue University. West Lafayette (1998)

  24. Van Vaerenbergh, S.: Kernel methods toolbox (KMBOX): a MATLAB toolbox for nonlinear signal processing and machine learning (2010). Software available at http://sourceforge.net/p/kmbox

  25. Chen, B., Principe, J.C.: Quantized kernel recursive least squares algorithm. IEEE Trans. Neural Netw. Learn. Syst. 24(9), 1484–1491 (2013)

    Article  Google Scholar 

  26. Liu, W., Park, I., Wang, Y., Jose, C.: Principe, extended kernel recursive least squares algorithm. IEEE Trans. Signal Process. 57(10), 3801–3814 (2009)

    Article  MathSciNet  Google Scholar 

  27. Chen, B., Zhao, S., Zhu, P., Principe, J.C.: Quantized kernel least mean square algorithm. IEEE Trans. Neural Netw. Learn. Syst. 23(1), 22–32 (2012)

    Article  Google Scholar 

  28. Zhu, P., Chen, B., Principe, J.C.: A novel extended kernel recursive least squares algorithm. Neural Netw. 32, 349–357 (2012)

  29. Wu, Z., Shi, J., Zhang, X., Ma, W., Chen, B.: Kernel recursive maximum correntropy. Signal Process. 117, 11–26 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Electronics, School of Engineering, Cochin University of Science and Technology, Kochi, India

    Jesna Anver & P. Abdulla

Authors
  1. Jesna Anver
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Abdulla
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jesna Anver.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Anver, J., Abdulla, P. Single-image super-resolution using kernel recursive least squares. SIViP 10, 1551–1558 (2016). https://doi.org/10.1007/s11760-016-0970-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0970-x

Keywords

Search

Navigation