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Efficient Face Image Deblurring via Robust Face Salient Landmark Detection

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Book cover Advances in Multimedia Information Processing -- PCM 2015 (PCM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9314))

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Abstract

Recent years have witnessed great progress in image deblurring. However, as an important application case, the deblurring of face images has not been well studied. Most existing face deblurring methods rely on exemplar set construction and candidate matching, which not only cost much computation time but also are vulnerable to possible complex or exaggerated face variations. To address the aforementioned problems, we propose a novel face deblurring method by integrating classical \(L_0\) deblurring approach with face landmark detection. A carefully tailored landmark detector is used to detect the main face contours. Then the detected contours are used as salient edges to guide the blind image deconvolution. Extensive experimental results demonstrate that the proposed method can better handle various complex face poses while greatly reducing computation time, as compared with state-of-the-art approaches.

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References

  1. Pan, J., Hu, Z., Su, Z., Yang, M.-H.: Deblurring face images with exemplars. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VII. LNCS, vol. 8695, pp. 47–62. Springer, Heidelberg (2014)

    Google Scholar 

  2. 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)

    Article  MathSciNet  Google Scholar 

  3. 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)

    Chapter  Google Scholar 

  4. Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28(5), 145 (2009)

    Article  Google Scholar 

  5. 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 

  6. 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)

    Article  Google Scholar 

  7. 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)

    Chapter  Google Scholar 

  8. HaCohen, Y., Shechtman, E., Lischinski, D.: Deblurring by example using dense correspondence. In: ICCV, pp. 2384–2391 (2013)

    Google Scholar 

  9. Hu, Z., Cho, S., Wang, J., Yang, M.H.: Deblurring low-light images with light streaks. In: CVPR, pp. 3382–3389 (2014)

    Google Scholar 

  10. Joshi, N., Szeliski, R., Kriegman, D.J.: PSF estimation using sharp edge prediction. In: CVPR, pp. 1–8 (2008)

    Google Scholar 

  11. Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: CVPR, pp. 2657–2664 (2011)

    Google Scholar 

  12. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR, pp. 1964–1971 (2009)

    Google Scholar 

  13. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: CVPR, pp. 2657–2664 (2011)

    Google Scholar 

  14. Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a con-ventional camera with a coded aperture. ACM Trans. Graph. 26(3), 70 (2007)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. Pan, J., Hu, Z., Su, Z., Yang, M.H.: Deblurring text images via L0-regularized intensity and gradient prior. In: CVPR (2014)

    Google Scholar 

  17. Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27(3), 73 (2008)

    Article  Google Scholar 

  18. Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based blur kernel estimation using patch priors. In: ICCP, pp. 1–8 (2013)

    Google Scholar 

  19. 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)

    Chapter  Google Scholar 

  20. Xu, L., Zheng, S., Jia, J.: Unnatural L0 sparse representation for natural image deblurring. In: CVPR, pp. 1107–1114 (2013)

    Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. 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 

  23. 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 

  24. 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)

    Chapter  Google Scholar 

  25. Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: CVPR, pp. 1867–1874 (2014)

    Google Scholar 

  26. Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. IJCV 107(2), 177–190 (2014)

    Article  MathSciNet  Google Scholar 

  27. Kazemi, V., Sullivan, J.: Face alignment with part-based modeling. In: BMVC, pp. 27–1 (2011)

    Google Scholar 

  28. Edwards, G.J., Cootes, T.F., Taylor, C.J.: Advances in active appearance models. In: ICCV, pp. 137–142 (1999)

    Google Scholar 

  29. Le, V., Brandt, J., Lin, Z., Bourdev, L., Huang, T.S.: Interactive facial feature localization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 679–692. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  30. Zhang, Q., Shen, X., Xu, L., Jia, J.: Interactive facial feature localization. In: ECCV, pp. 815–830 (2014)

    Google Scholar 

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Acknowledgement

This work was supported in part by the National Science Foundation of China No. 61472103, and Key Program Grant of National Science Foundation of China No. 61133003.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Yinghao Huang, Hongxun Yao, Sicheng Zhao & Yanhao Zhang

Authors
  1. Yinghao Huang
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  2. Hongxun Yao
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  3. Sicheng Zhao
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  4. Yanhao Zhang
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Corresponding author

Correspondence to Hongxun Yao .

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Jitao Sang

  3. ICU, IVY Lab, KAIST, Daejeon, Korea (Republic of)

    Yong Man Ro

  4. KAIST, Daejeon, Korea (Republic of)

    Junmo Kim

  5. College of Computer Science, Zhejiang University, Hangzhou, China

    Fei Wu

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Huang, Y., Yao, H., Zhao, S., Zhang, Y. (2015). Efficient Face Image Deblurring via Robust Face Salient Landmark Detection. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_2

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  • DOI: https://doi.org/10.1007/978-3-319-24075-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24074-9

  • Online ISBN: 978-3-319-24075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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