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Towards more efficient and flexible face image deblurring using robust salient face landmark detection

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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, shapes and expressions while greatly reducing computation time, as compared with existing state-of-the-art approaches.

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References

  1. Belhumeur PN, David WJ, David JK, Narendra K (2013) Localizing parts of faces using a consensus of exemplars. PAMI 35(12):2930–2940

    Article  Google Scholar 

  2. Cai JF, Ji H, Liu C, Shen Z (2012) Framelet based blind motion deblurring from a single image. IEEE Trans. Image Process 21(2):562–572

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  4. Cho H, Wang J, Lee S (2012) Text image deblurring using text-specific properties. In: ECCV, pp 524– 537

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

    Article  Google Scholar 

  6. Cho TS, Paris S, Horn BKP, Freeman WT (2011) Blur kernel estimation using the radon transform. In: CVPR, pp 241–248

  7. Edwards GJ, Cootes TF, Taylor CJ (1999) Advances in active appearance models. In: ICCV, pp 137–142

  8. Fergus R, Singh B, Hertzmann A, Roweis ST, Freeman WT (2006) Removing camera shake from a single photograph. ACM Trans Graph 25(3):787–794

    Article  Google Scholar 

  9. Goldstein A, Fattal R (2012) Blur-kernel estimation from spectral irregularities. In: ECCV, pp 622–635

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

  11. He K, Sun J, Tang X (2010) Guided image filtering. In: ECCV, pp 1-14

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

  13. Joshi N, Szeliski R, Kriegman DJ (2008) PSF estimation using sharp edge prediction. In: CVPR, pp 1-8

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

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

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

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

    Article  Google Scholar 

  18. Levin A, Weiss Y, Durand F, Freeman WT (2009) Understanding and evaluating blind deconvolution algorithms. In: CVPR, pp 1964-1971

  19. Levin A, Weiss Y, Durand F, Freeman WT (2011) Efficient marginal likelihood optimization in blind deconvolution. In: CVPR, pp 2657-2664

  20. Le V, Brandt J, Lin Z, Bourdev L, Huang TS (2012) Interactive facial feature localization. In. ECCV, pp 679-692

  21. Le V, Jonathan B, Zhe L, Lubomir B, Thomas SH (2012) Interactive facial feature localization. In: ECCV, pp 679-692

  22. Michaeli T, Michal I (2014) Blind deblurring using internal patch recurrence. In: ECCV, pp 783–798

  23. Nishiyama M, Hadid A, Takeshima H, Shotton J, Kozakaya T, Yamaguchi O (2011) Facial deblur inference using subspace analysis for recognition of blurred faces. IEEE Trans Pattern Anal Mach Intell 33(4):838–845

    Article  Google Scholar 

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

  25. Pan J, Hu Z, Su Z, Yang MH (2014) Deblurring Face Images with Exemplars. In: ECCV, pp 47-62

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

    Article  Google Scholar 

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

  28. Xu L, Jia J (2010) Two-phase kernel estimation for robust motion deblurring. In: ECCV, pp 157-170

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

  30. Yitzhaky Y, Mor I, Lantzman A, Kopeika NS (1998) Direct method for restoration of motion-blurred images. J Opt Soc Am A 15(6):1512–1519

    Article  Google Scholar 

  31. Zhang H, Yang J, Zhang Y, Huang TS (2011) Close the loop: joint blind image restoration and recognition with sparse representation prior. In: ICCV, pp 770- 777

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

  33. Zhong L, Cho S, Metaxas D, Paris S, Wang J (2013) Handling noise in single image deblurring using directional filters. In: CVPR, pp 612-619

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Acknowledgments

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.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Dazhi Street, Harbin, 150001, China

    Yinghao Huang, Hongxun Yao, Sicheng Zhao & Yanhao Zhang

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  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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Correspondence to Yinghao Huang.

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Huang, Y., Yao, H., Zhao, S. et al. Towards more efficient and flexible face image deblurring using robust salient face landmark detection. Multimed Tools Appl 76, 123–142 (2017). https://doi.org/10.1007/s11042-015-3009-3

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  • DOI: https://doi.org/10.1007/s11042-015-3009-3

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