Skip to main content
Springer Nature Link
Log in

Learning adaptive interpolation kernels for fast single-image super resolution

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

Abstract

This paper presents a fast single-image super-resolution approach that involves learning multiple adaptive interpolation kernels. Based on the assumptions that each high-resolution image patch can be sparsely represented by several simple image structures and that each structure can be assigned a suitable interpolation kernel, our approach consists of the following steps. First, we cluster the training image patches into several classes and train each class-specific interpolation kernel. Then, for each input low-resolution image patch, we select few suitable kernels of it to make up the final interpolation kernel. Since the proposed approach is mainly based on simple linear algebra computations, its efficiency can be guaranteed. And experimental comparisons with state-of-the-art super-resolution reconstruction algorithms on simulated and real-life examples can validate the performance of our proposed approach.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Notes

  1. The source codes of our proposed SRR approach can be downloaded at http://mda.ia.ac.cn/people/huxy/oproj/fastsrr.htm.

  2. The codes for the SP-SR method used for comparison can be downloaded from the authors’ homepage at http://www.ifp.illinois.edu/~jyang29/resources.html. The RMSE and SSIM values of pictures flower and girl are copied from [12] directly.

References

  1. Tian, J., Ma, K.K.: A survey on super-resolution imaging. Signal Image Video Process. 5(3), 329–342 (2011)

    Article  MathSciNet  Google Scholar 

  2. Farsiu, S., Robinson, D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13(10), 1327–1345 (2004)

    Article  Google Scholar 

  3. Liu, Z., Wang, H., Peng, S.: Image magnification method using joint diffusion. J. Comput. Sci. Technol. 19(5), 698–707 (2004)

    Article  Google Scholar 

  4. Takeda, H., Farsiu, S., Milanfar, P.: Kernel regression for image processing and reconstruction. IEEE Trans. Image Process. 16(2), 349–366 (2007)

    Article  MathSciNet  Google Scholar 

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

    Google Scholar 

  6. Sun, J., Xu, Z., Shum, H.Y.: Image super-resolution using gradient profile prior. In: IEEE conference on computer vision and pattern recognition, Anchorage, AK, 23–28 June 2008, pp. 1–8 (2008)

  7. Anbarjafari, G., Demirel, H.: Image super resolution based on interpolation of wavelet domain high frequency subbands and the spatial domain input image. ETRI J. 32(3), 390–394 (2010)

    Article  Google Scholar 

  8. Shao, W.Z., Deng, H.S., Wei, Z.H.: A posterior mean approach for MRF-based spatially adaptive multi-frame image super-resolution. Signal Image Video Process. 1–13 (2013). doi:10.1007/s11760-013-0458-x

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

    Article  MATH  Google Scholar 

  10. Freeman, W.T., Jones, T., Pasztor, E.: Example-based super-resolution. IEEE Comput. Gr. Appl. 22(2), 56–65 (2002)

    Article  Google Scholar 

  11. Yang, J., Wright, J., Huang, T.S., Ma,Y.: Image super resolution as sparse representation of raw image patches. In: IEEE conference on computer vision and pattern recognition, Anchorage, AK, 23–28 June 2008, pp. 1–8 (2008)

  12. Yang, J., Wright, J., Huang, T.S.: Image super resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  13. Glasner, D., Bagon, S., Irani, M.: Super-resolution from a single image. In: IEEE 12th international conference on computer vision, Kyoto, 29 Sep–2 Oct 2009, pp. 349–356 (2009)

  14. Freedman, G., Fattal, R.: Image and video upscaling from local self-examples. ACM Trans. Gr. (TOG) 30(2), 12:1–12:11 (2011)

    Google Scholar 

  15. Damkat, C.: Single image super-resolution using self-examples and texture synthesis. Signal Image Video Process. 5(3), 343–352 (2011)

    Google Scholar 

  16. Chang, H., Yeung, D.Y., Xiong, Y.: Super-resolution through neighbor embedding. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Washington, DC, 27 June– 2 July 2004, pp. 275–282 (2004)

  17. Zhang, H., Zhang, Y., Huang, T.S.: Efficient sparse representation based image super resolution via dual dictionary learning. In: IEEE international conference on multimedia and expo, Barcelona, Spain, 11–15 July 2011, pp. 1–6 (2011)

  18. Basso, C., Santoro, M., Verri, A., Villa, S.: PADDLE: proximal algorithm for dual dictionaries learning. In: Artificial neural networks and machine learning C ICANN 2011. Lecture notes in computer science, vol. 6791, pp. 379–386 (2011)

  19. Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “nearest neighbor” meaningful. In: Database theory ICDT 99. Lecture notes in computer science, vol. 1540, pp. 217–235 (1999)

  20. Donoho, D.L.: For most large underdetermined systems of linear equations, the minimal \(\ell ^{1}\)-norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59(6), 797–829 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  21. Donoho, D.L.: For most large underdetermined systems of linear equations, the minimal \(\ell ^{1}\)-norm near-solution approximates the sparsest near-solution. Commun. Pure Appl. Math. 59(7), 907–934 (2006)

    Article  MathSciNet  Google Scholar 

  22. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  23. Qi, S., Li, Z., Jia, J., Tang, C.K.: Fast image/video upsampling. ACM Trans. Gr. (TOG) 27(5), 153:1–153:7 (2008)

    Google Scholar 

Download references

Acknowledgments

The authors wish to thank the anonymous reviewers for their insightful comments, which helped us improve the quality of the paper significantly

Author information

Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing , 100190, People’s Republic of China

    Xiyuan Hu & Silong Peng

  2. Institute of Information Science, Academia Sinica, Taipei , 11529, Taiwan, ROC

    Wen-Liang Hwang

  3. Department of Information Management, Kainan University, Taoyuan County , 33857, Taiwan, ROC

    Wen-Liang Hwang

Authors
  1. Xiyuan Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Silong Peng
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Wen-Liang Hwang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Silong Peng.

Additional information

This work was supported by the National Natural Science Foundation of China (61032007, 61101219, 61201375) and the National High Technology R&D Program of China (863 Program) (Grant No. 2013AA014602)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hu, X., Peng, S. & Hwang, WL. Learning adaptive interpolation kernels for fast single-image super resolution. SIViP 8, 1077–1086 (2014). https://doi.org/10.1007/s11760-014-0634-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-014-0634-7

Keywords