Deblurring Face Images with Exemplars

  • Jinshan Pan
  • Zhe Hu
  • Zhixun Su
  • Ming-Hsuan Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8695)


The human face is one of the most interesting subjects involved in numerous applications. Significant progress has been made towards the image deblurring problem, however, existing generic deblurring methods are not able to achieve satisfying results on blurry face images. The success of the state-of-the-art image deblurring methods stems mainly from implicit or explicit restoration of salient edges for kernel estimation. When there is not much texture in the blurry image (e.g., face images), existing methods are less effective as only few edges can be used for kernel estimation. Moreover, recent methods are usually jeopardized by selecting ambiguous edges, which are imaged from the same edge of the object after blur, for kernel estimation due to local edge selection strategies. In this paper, we address these problems of deblurring face images by exploiting facial structures. We propose a maximum a posteriori (MAP) deblurring algorithm based on an exemplar dataset, without using the coarse-to-fine strategy or ad-hoc edge selections. Extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm for deblurring face images. We also show the extendability of our method to other specific deblurring tasks.


Face Image Kernel Estimation Error Ratio Ringing Artifact Blur Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Cai, J.F., Ji, H., Liu, C., Shen, Z.: Framelet based blind motion deblurring from a single image. IEEE Trans. Image Process. 21(2), 562–572 (2012)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Cho, H., Wang, J., Lee, S.: Text image deblurring using text-specific properties. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 524–537. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28(5), 145 (2009)CrossRefGoogle Scholar
  4. 4.
    Cho, T.S., Paris, S., Horn, B.K.P., Freeman, W.T.: Blur kernel estimation using the radon transform. In: CVPR, pp. 241–248 (2011)Google Scholar
  5. 5.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25(3), 787–794 (2006)CrossRefGoogle Scholar
  6. 6.
    Goldstein, A., Fattal, R.: Blur-kernel estimation from spectral irregularities. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 622–635. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Gross, R., Matthews, I., Cohn, J.F., Kanade, T., Baker, S.: Multi-pie. In: FG, pp. 1–8 (2008)Google Scholar
  8. 8.
    HaCohen, Y., Shechtman, E., Lischinski, D.: Deblurring by example using dense correspondence. In: ICCV, pp. 2384–2391 (2013)Google Scholar
  9. 9.
    He, K., Sun, J., Tang, X.: Guided image filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Hu, Z., Cho, S., Wang, J., Yang, M.-H.: Deblurring low-light images with light streaks. In: CVPR, pp. 3382–3389 (2014)Google Scholar
  11. 11.
    Hu, Z., Yang, M.-H.: Good regions to deblur. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 59–72. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Joshi, N., Szeliski, R., Kriegman, D.J.: PSF estimation using sharp edge prediction. In: CVPR, pp. 1–8 (2008)Google Scholar
  13. 13.
    Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: CVPR, pp. 2657–2664 (2011)Google Scholar
  14. 14.
    Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR, pp. 1964–1971 (2009)Google Scholar
  15. 15.
    Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: CVPR, pp. 2657–2664 (2011)Google Scholar
  16. 16.
    Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26(3), 70 (2007)Google Scholar
  17. 17.
    Nishiyama, M., Hadid, A., Takeshima, H., Shotton, J., Kozakaya, T., Yamaguchi, O.: Facial deblur inference using subspace analysis for recognition of blurred faces. IEEE Trans. Pattern Anal. Mach. Intell. 33(4), 838–845 (2011)CrossRefGoogle Scholar
  18. 18.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst., Man, and Cybern. 9(9), 62–66 (1979)Google Scholar
  19. 19.
    Pan, J., Hu, Z., Su, Z., Yang, M.-H.: Deblurring text images via L0-regularized intensity and gradient prior. In: CVPR, pp. 2901–2908 (2014)Google Scholar
  20. 20.
    Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27(3), 73 (2008)CrossRefGoogle Scholar
  21. 21.
    Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based blur kernel estimation using patch priors. In: ICCP, pp. 1–8 (2013)Google Scholar
  22. 22.
    Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via L0 gradient minimization. ACM Trans. Graph. 30(6), 174 (2011)Google Scholar
  24. 24.
    Xu, L., Zheng, S., Jia, J.: Unnatural L0 sparse representation for natural image deblurring. In: CVPR, pp. 1107–1114 (2013)Google Scholar
  25. 25.
    Yitzhaky, Y., Mor, I., Lantzman, A., Kopeika, N.S.: Direct method for restoration of motion-blurred images. J. Opt. Soc. Am. A 15(6), 1512–1519 (1998)CrossRefGoogle Scholar
  26. 26.
    Zhang, H., Yang, J., Zhang, Y., Huang, T.S.: Close the loop: joint blind image restoration and recognition with sparse representation prior. In: ICCV, pp. 770–777 (2011)Google Scholar
  27. 27.
    Zhong, L., Cho, S., Metaxas, D., Paris, S., Wang, J.: Handling noise in single image deblurring using directional filters. In: CVPR, pp. 612–619 (2013)Google Scholar
  28. 28.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: CVPR, pp. 2879–2886 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jinshan Pan
    • 1
  • Zhe Hu
    • 2
  • Zhixun Su
    • 1
  • Ming-Hsuan Yang
    • 2
  1. 1.Dalian University of TechnologyDalianChina
  2. 2.University of CaliforniaMercedUSA

Personalised recommendations