Skip to main content
SpringerLink
Log in

Face hallucination using example-based regularization

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Face super-resolution is to synthesize a high resolution facial image from a low resolution input, which can significantly improve the recognition for computer and human. Regularization plays a vital role in ill-posed problems. The use of examples becomes much more effective when handling narrow family of images, such as face images. A properly chosen regularization can direct the solution toward a better quality outcome. An emerging powerful regularization is one that leans on image examples. This paper proposed a face hallucination method using example-based regularization. The work is specially targeted at improving the quality of high magnification. Our work follows the pyramid framework and assigns several high-quality candidate patches for each location in the degraded image. All problematic examples are rejected by defining an error function which embodies the example-based regularization. After repeated pruning, the reconstruction is done when there is only one candidate patch left in each location. The encouraging experimental results provide some hints that our approach is effective.

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. Park S-C, Park M-K, Kang M-G (2003) Super-resolution image reconstruction: a technical overview (J). IEEE Signal Process Mag 20(3):21–36

    Article  Google Scholar 

  2. Hertzmann A, Jacobs C-E, Oliver N et al. (2001) Image analogies. In: Proceedings of the SIGGRAPH. Los Angeles, USA, ACM Press, New York, pp 327–340

  3. Elad M, Feuer A (1999) Super-resolution restoration of an image sequence: adaptive filtering approach. IEEE Trans Image Process 8(3):387–395

    Article  Google Scholar 

  4. Tian J, Ma K (2011) A survey on super-resolution imaging. SIViP 5(3):1–14

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  6. Nguyen N, Milanfar P (2000) A wavelet-based interpolation-restoration method for super-resolution. Circuits Syst Signal Process 19(4):321–338

    Article  MATH  Google Scholar 

  7. Jian Sun, Zong-ben Xu, Heung-Yeung Shum (2008) Image super-resolution using gradient profile prior. In: Proceedings of the 10th international conference on computer vision and pattern recognition, anchorage, Alaska, USA, 24–26 June 2008, pp 1–8

  8. Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: Proceedings of the 12th international conference on computer vision, Kyoto, Japan, 29 Sept–2 Oct 2009, pp 349–356

  9. Lyndsey C-P, David P-C, Stephen J-R, Andrew Z (2009) Bayesian methods for image super-resolution. Comput J 52(1):101–113

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  11. Farsiu S, Robinson M-D, Elad M et al (2004) Fast and robust multiframe super resolution. IEEE Trans Image Process 13(10):1327–1344

    Article  Google Scholar 

  12. Tsai R-Y, Huang T-S (1984) Multiple frame image restoration and registration. Adv Comput Vis Image Process 1:317–339

    Google Scholar 

  13. Ur H, Gross D (1992) Improved resolution from sub-pixel shifted pictures. IEEE Trans Graph Models Image Process 54(2):181–186

    Article  Google Scholar 

  14. Nguyen N, Milanfar P (2000) An efficient wavelet-based algorithm for image super-resolution. In: Proceedings of international conference on image processing, Vancouver, BC, Canada, 10–13 Sept 2000, pp 351–354

  15. Irani M, Peleg S (1991) Improving resolution by image registration. IEEE Trans Graph Models Image Process 53(3):231–239

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Elad M, Feuer A (1997) Restoration of a single super-resolution image from several blurred, noisy, and under-sampled measured images. IEEE Trans Image Process 6(12):1646–1658

    Article  Google Scholar 

  18. Hardie R-C, Barnard K-J, Armstrong E–E (1997) Joint MAP registration 245 and high-resolution image estimation using a sequence of under-sampled images. IEEE Trans Image Process 6:1621–1633

    Article  Google Scholar 

  19. Baker S, Kanade T (2002) Limits on super-resolution and how to break them. IEEE Trans PAMI 24(9):1167–1183

    Article  Google Scholar 

  20. Baker S, Kanade T (2000) Hallucinating faces. In: Proceedings of the 4th international conference on automatic face and gesture recognition, Grenoble, France, 28–30 Mar 2000, pp 83–88

  21. Freeman W-T, Pasztor E-C (1999) Learning low-level vision. In: Proceedings of the 7th international conference on computer vision, Kerkyra, Corfu, 20–25 Sept 1999, pp 1182–1189

  22. Freeman W-T, Rjones T, Pasztor E-C (2002) Example-based super-resolution. In: Proceedings of the 10th international conference on computer graphics and applications, Beijing, China, 9–11 Oct 2002, pp 56–65

  23. Suresh K-V, Rajagopalan A-N (2007) Robust and computationally efficient super-resolution algorithm. J Opt Soc Am A Opt Image Sci Vis 24(4):984–992

    Article  Google Scholar 

  24. Li X, Hu Y, Gao X et al (2010) A multi-frame image super-resolution method. IEEE Signal Process Mag 90(2):405–414

    Article  MATH  Google Scholar 

  25. Shen H, Zhang L, Huang B et al (2007) A MAP approach for joint motion estimation, segmentation, and super resolution. IEEE Trans Image Process 16(2):479–490

    Article  MathSciNet  Google Scholar 

  26. El-Khamy S-E, Hadhoud M-M, Dessouky M-I et al (2006) Wavelet fusion: a tool to break the limits on LMMSE image super-resolution. Int J Wavelets Multiresolut Inf Process 4(1):105–118

    Article  MathSciNet  MATH  Google Scholar 

  27. Farsiu S, Robinson D, Elad M et al (2004) Advances and challenges in super-resolution. Int J Imaging Syst Technol 16(2):47–57

    Article  Google Scholar 

  28. Mohammad-Djafari A (2009) Super-resolution: a short review, a new method based on hidden Markov modeling of HR image and future challenges. Comput J 52(1):126–141

    Article  Google Scholar 

  29. Sun J, Zhang N, Tao H, Shum H (2003) Image hallucination with primal sketch priors. In: Proceedings of international conference on computer vision and pattern recognition, Madison, Wisconsin, 16–22 June 2003, pp 729–736

  30. Hong C, Dit-Yan Y, Yimin X (2004) Super-resolution through neighbor embedding. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, Washington DC, USA, 27 June–2 July 2004, pp 275–282

  31. Yi T, Pingkun Y, Yuan Y, Xue-long L (2011) Single-image super-resolution via local learning. Int J Mach Learn Cybern 2(1):15–23

    Article  Google Scholar 

  32. Chakrabarti A, Rajagopalan A-N, Chellappa R (2007) Super-resolution of face images using kernel PCA-based prior. IEEE Trans Multimedia 9(4):888–892

    Article  Google Scholar 

  33. Bruce P, Ashraful A-M, Hong Y (2011) Performance evaluation and comparison of PCA based human face recognition methods for distorted images. Int J Mach Learn Cybern 2(4):245–259

    Article  Google Scholar 

  34. Xiang X, Wanquan L, Svetha V (2011) An innovative face image enhancement based on principle component analysis. Int J Mach Learn Cybern. doi: 10.1007/s13042-011-0060-x

  35. Chacko Binu P, Vimal Krishnan VR, Raju G, Babu Anto P (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybern 3(2):149–161

    Article  Google Scholar 

  36. Malay KK, Manish Ch, Minakshi B (2011) Interactive image retrieval using M-band wavelet, earth mover’s distance and fuzzy relevance feedback. Int J Mach Learn Cybern. doi: 10.1007/s13042-011-0062-8

  37. Wang J, Zhu S, Gong Y (2010) Resolution enhancement based on learning the sparse association of image patches. Pattern Recogn Lett 31(1):1–10

    Article  Google Scholar 

  38. Kim K, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Trans Pattern Anal Mach Intell 32(6):1127–1133

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  40. Liu L, YiDing W (2008) Face super-resolution using a hybrid model. In: Proceedings of international conference on signal processing, Beijing, China, 26–29 Oct 2008, pp 1153–1156

  41. Liu C, Shum H, Freeman W (2007) Face hallucination: theory and practice. Int J Comput Vis 75:115–134

    Article  Google Scholar 

  42. Wang X, Tang X (2005) Hallucinating face by eigen-transformation. IEEE Trans Syst Man Cybern 35(3):425–434

    Article  Google Scholar 

  43. Cong-yong S, Yue-ting Z, Li H, Fei W (2005) Steerable pyramid-based face hallucination. IEEE Trans Pattern Recogn 38:813–824

    Article  Google Scholar 

  44. Dsenko D, Elad M (2007) Example-based single document image super-resolution: a global MAP approach with outlier rejection. Int J Multidimens Syst Signal Process 18(2):103–121

    Article  Google Scholar 

  45. Lagendijk R-L, Biemond J (1991) Iterative identification and restoration of images. Kluwer Academic Publishers, Boston

    Book  MATH  Google Scholar 

  46. Roth S, Black M-J (2005) Fields of experts: a framework for learning image priors. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition (CVPR), San-Diego, California, 20–25 June 2005, pp 860–867

  47. Efros A-A, Leung T-K (1999) Texture synthesis by non-parametric sampling. In: Proceedings of IEEE computer society conference on computer vision (ICCV), Corfu, Greece, 20–25 Sept 1999, pp 1033–1038

  48. Nakagaki R, Katsaggelos A-K (2003) VQ-based blind image restoration algorithm. IEEE Trans Image Process 12(9):1044–1053

    Article  Google Scholar 

  49. Criminisi A, Perez P, Toyama K (2004) Region filling and object removal by exemplar-based image in painting. IEEE Trans Image Process 13(9):1200–1212

    Article  Google Scholar 

  50. Philips P-J, Moon H, Rauss P, Rizvi S-A (1997) The FERET evaluation methodology for face-recognition algorithms. In: Proceedings of international conference on computer vision and pattern recognition, San Juan, Puerto Rico, 17–19 June 1997, pp 137–143

Download references

Acknowledgments

This paper is supported by Natural Science Foundation of China (61273273), Beijing Natural Science Foundation (4112050) and Research Fund for Doctoral Program of Higher Education of China. The authors would like to thank Zhen gang Zhai, Zi ye Yan and Yao zu An for the insightful discussions. This work is also supported by the Natural Science Foundation of Hebei Province (F2012201023) and Shenzhen Key Laboratory for High Performance Data Mining with Shenzhen New Industry Development Fund (CXB201005250021A).

Author information

Authors and Affiliations

  1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science, Beijing Institute of Technology, Beijing, 100081, China

    Hong Zhao & Yao Lu

  2. College of Mathematics and Computer Science, Hebei University, Baoding, 071002, China

    Hong Zhao

Authors
  1. Hong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Zhao.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zhao, H., Lu, Y. Face hallucination using example-based regularization. Int. J. Mach. Learn. & Cyber. 4, 693–701 (2013). https://doi.org/10.1007/s13042-012-0149-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-012-0149-x

Keywords

Search

Navigation