Skip to main content
SpringerLink
Log in

Single Face Image Super-Resolution via Multi-dictionary Bayesian Non-parametric Learning

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9489))

Included in the following conference series:

  • 2128 Accesses

Abstract

The face image super-resolution is a domain specific problem. Human face has complex, and fixed domain specific priors, which should be detail explored in super-resolution algorithm. This paper proposes an effective single image face super-resolution method by pre-clustering training data and Bayesian non-parametric learning. After pre-clustering, face patches from different clusters represent different areas in face, and also offer specific priors on these areas. Bayesian non-parametric learning captures consistent and accurate mapping between coupled spaces. Experimental results show that our method produces competitive results to other state-of-the-art methods, with much less computational time.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wang, N., Tao, D., Gao, X., Li, X., Li, J.: A comprehensive survey to face hallucination. Int. J. Comput. Vis. (IJCV) 106(1), 9–30 (2014)

    Article  Google Scholar 

  2. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution as sparse representation of raw image patches. In: Proceedings of CVPR, pp. 1–8 (2008)

    Google Scholar 

  3. Yang, J., Wang, Z., Lin, Z., Huang, T.: Coupled dictionary training for image super-resolution. IEEE Trans. Image Process. (TIP) 21(8), 3467–3478 (2012)

    Article  MathSciNet  Google Scholar 

  4. Yang, J., Wright, J., Huang, T., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. (TIP) 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  5. Wang, S., Zhang, L., Liang, Y., Pan, Q.: Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. In: Proceedings of CVPR, pp. 1–8 (2012)

    Google Scholar 

  6. Yang, J., Wang, Z., Lin, Z., Shu, X., Huang, T.: Bilevel sparse coding for coupled feature spaces. In: Proceedings of CVPR, pp. 2360–2367 (2012)

    Google Scholar 

  7. Paisley, J., Carin, L.: Nonparametric factor analysis with beta process priors. In: Proceedings of ICML, pp. 777–784, (2009)

    Google Scholar 

  8. Zhou, M., Chen, H., Paisley, J., Ren, L., Li, L., Xing, Z., Dunson, D., Sapiro, G., Carin, L.: Nonparametric bayesian dictionary learning for analysis of noisy and incomplete images. IEEE Trans. Image Process. (TIP) 21(1), 130–144 (2012)

    Article  MathSciNet  Google Scholar 

  9. He, L., Qi, H., Zaretzki, R.: Beta process joint dictionary learning for coupled feature spaces with application to single image super-resolution. In Proceedings of CVPR, pp. 1–8 (2013)

    Google Scholar 

  10. Polatkan, G., Zhou, M., Carin, L., Blei, D., Daubechies, I.A.: Bayesian nonparametric approach to image super-resolution. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 37, 346–358 (2015)

    Article  Google Scholar 

  11. Yang, C.-Y., Ma, C., Yang, M.-H.: Single-image super-resolution: a benchmark. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 372–386. Springer, Heidelberg (2014)

    Google Scholar 

  12. Yang, C.-Y., Liu, S., Yang, M.-H.: Structured face hallucination. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1099–1106 (2013)

    Google Scholar 

  13. Ha Cohen, Y., Fattal, R., Lischinski, D.: Image up-sampling via texture hallucination. In: ICCP (2010)

    Google Scholar 

  14. Jiang, J., Hu, R., Han, Z., Lu, T., Huang, K.: Position-patch based face hallucination via locality-constrained representation. IEEE International Conference on Multimedia and Expo, pp. 212–217 (2012)

    Google Scholar 

  15. Jung, C., Jiao, L., Liu, B., Gong, M.: Position-patch based face hallucination using convex optimization. IEEE Sig. Process. Lett. 18(6), 367–370 (2011)

    Article  Google Scholar 

  16. Buades, A., Coll, B., Morel, J.: A non-local algorithm for Image denoising. In: CVPR, vol. 2, pp. 60–65. IEEE (2005)

    Google Scholar 

  17. Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: ICCV. IEEE (2009)

    Google Scholar 

Download references

Acknowledgements

This research has been supported by funding from National Natural Science Foundation of China (61202168, and 61201234,61472278), Key project of Natural Science Foundation of Tianjin (14JCZDJC31700), and Tianjin Education Committee science and technology development Foundation (No. 20120802, 20130704).

Author information

Authors and Affiliations

  1. Key Laboratory of Computer Vision and System, Tianjin University of Technology, Tianjin, China

    Jingjing Wu, Hua Zhang, Yanbing Xue, Mian Zhou, Guangping Xu & Zan Gao

  2. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China

    Jingjing Wu, Hua Zhang, Yanbing Xue, Mian Zhou, Guangping Xu & Zan Gao

Authors
  1. Jingjing Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanbing Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mian Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guangping Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zan Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanbing Xue .

Editor information

Editors and Affiliations

  1. University of Istanbul, Istanbul, Turkey

    Sabri Arik

  2. University at Qatar, Doha, Qatar

    Tingwen Huang

  3. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Weng Kin Lai

  4. University of Science Technology, Wuhan, China

    Qingshan Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Wu, J., Zhang, H., Xue, Y., Zhou, M., Xu, G., Gao, Z. (2015). Single Face Image Super-Resolution via Multi-dictionary Bayesian Non-parametric Learning. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-26532-2_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26531-5

  • Online ISBN: 978-3-319-26532-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation