Advertisement

Machine Vision and Applications

, Volume 28, Issue 3–4, pp 431–444 | Cite as

Local motion deblurring using an effective image prior based on both the first- and second-order gradients

  • Taiebeh Askari Javaran
  • Hamid Hassanpour
  • Vahid Abolghasemi
Original Paper
  • 353 Downloads

Abstract

Local motion deblurring is a highly challenging problem as both the blurred region and the blur kernel are unknown. Most existing methods for local deblurring require a specialized hardware, an alpha matte, or user annotation of the blurred region. In this paper, an automatic method is proposed for local motion deblurring in which a segmentation step is performed to extract the blurred region. Then, for blind deblurring, i.e., simultaneously estimating both the blur kernel and the latent image, an optimization problem in the form of maximum-a-posteriori (MAP) is introduced. An effective image prior is used in the MAP based on both the first- and second-order gradients of the image. This prior assists to well reconstruct salient edges, providing reliable edge information for kernel estimation, in the intermediate latent image. We examined the proposed method for both global and local deblurring. The efficiency of the proposed method for global deblurring is demonstrated by performing several quantitative and qualitative comparisons with the state-of-the-art methods, on both a benchmark image dataset and real-world motion blurred images. In addition, in order to demonstrate the efficiency in local motion deblurring, the proposed method is examined to deblur some real-world locally linear motion blurred images. The qualitative results show the efficiency of the proposed method for local deblurring at various blur levels.

Keywords

Local motion deblurring Global motion deblurring Blur map Blind image deblurring Image prior Salient edges Image reconstruction Kernel estimation 

References

  1. 1.
    Cho, S., Lee, S.: Fast motion deblurring. In: ACM Transactions on Graphics (TOG), vol. 28. ACM, New York (2009)Google Scholar
  2. 2.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. (TOG) 25(3), 787–794 (2006)CrossRefGoogle Scholar
  3. 3.
    Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on IEEE, 2011, pp. 233–240Google Scholar
  4. 4.
    Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. In: ACM Transactions on Graphics (TOG), vol. 27, ACM, New York (2008)Google Scholar
  5. 5.
    Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based blur kernel estimation using patch priors. In: 2013 IEEE International Conference on Computational Photography (ICCP), pp. 1–8 (2013)Google Scholar
  6. 6.
    Xu, L., Jia, J.: Two-Phase Kernel Estimation for Robust Motion Deblurring. Springer, New York (2010)CrossRefGoogle Scholar
  7. 7.
    Xu, L., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1107–1114 (2013)Google Scholar
  8. 8.
    Joshi, N., Szeliski, R., Kriegman, D.J.: PSF estimation using sharp edge prediction. In: Computer Vision and Pattern Recognition, 2008. IEEE Conference on CVPR 2008, pp. 1–8 (2008)Google Scholar
  9. 9.
    Cho, T.S., Paris, S., Horn, B.K., Freeman, W.T.: Blur kernel estimation using the radon transform. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on IEEE, pp. 241–248 (2011)Google Scholar
  10. 10.
    Pan, J., Liu, R., Su, Z., Liu, G.: Motion blur kernel estimation via salient edges and low rank prior. In: 2014 IEEE International Conference on Multimedia and Expo (ICME), IEEE, pp. 1–6 (2014)Google Scholar
  11. 11.
    Pan, J., Liu, R., Su, Z., Gu, X.: Kernel estimation from salient structure for robust motion deblurring. Sign. Process. Image Commun. 28(9), 1156–1170 (2013)CrossRefGoogle Scholar
  12. 12.
    Pan, J., Su, Z.: Fast-regularized kernel estimation for robust motion deblurring. IEEE Sign. Process. Lett. 20(9), 841–844 (2013)CrossRefGoogle Scholar
  13. 13.
    Cai, J.-F., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: Computer Vision and Pattern Recognition, 2009. IEEE Conference on CVPR 2009, IEEE, pp. 104–111Google Scholar
  14. 14.
    Chen, J., Yuan, L., Tang, C.-K., Quan, L.: Robust dual motion deblurring. In: Computer Vision and Pattern Recognition, 2008. IEEE Conference on CVPR 2008, IEEE, pp. 1–8Google Scholar
  15. 15.
    H. Hong, I. K. Park, Single image motion deblurring using anisotropic regularization. In: 2010 17th IEEE International Conference on Image Processing (ICIP), IEEE, pp. 1149–1152Google Scholar
  16. 16.
    Li, W., Zhang, J., Dai, Q.-H.: Robust blind motion deblurring using near-infrared flash image. J. Vis. Commun. Image Represent. 24(8), 1394–1413 (2013)CrossRefGoogle Scholar
  17. 17.
    Dai, S., Wu, Y.: Removing partial blur in a single image. In: Computer Vision and Pattern Recognition, 2009. IEEE Conference on CVPR 2009, IEEE, pp. 2544–2551Google Scholar
  18. 18.
    Martinello, M., Favaro, P.: Fragmented aperture imaging for motion and defocus deblurring. In: 2011 18th IEEE International Conference on Image Processing (ICIP), IEEE, pp. 3413–3416Google Scholar
  19. 19.
    Levin, A.: Blind motion deblurring using image statistics. In: Advances in Neural Information Processing Systems, 2006, pp. 841–848Google Scholar
  20. 20.
    Couzinie-Devy, F., Sun, J., Alahari, K., Ponce, J.: Learning to estimate and remove non-uniform image blur. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1075–1082Google Scholar
  21. 21.
    Raskar, R., Agrawal, A., Tumblin, J.: Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans. Graph. (TOG) 25(3), 795–804 (2006)CrossRefGoogle Scholar
  22. 22.
    Levin, A., Fergus, R., Durand, F., Freeman, W.T. : Image and depth from a conventional camera with a coded aperture. In: ACM Transactions on Graphics (TOG), Vol. 26, p. 70, ACM (2007)Google Scholar
  23. 23.
    Tai, Y.-W., Kong, N., Lin, S., Shin, S.Y.: Coded exposure imaging for projective motion deblurring. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 2408–2415Google Scholar
  24. 24.
    Tai, Y.-W., Du, H., Brown, M.S., Lin, S.: Correction of spatially varying image and video motion blur using a hybrid camera. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1012–1028 (2010)CrossRefGoogle Scholar
  25. 25.
    Shan, Q., Xiong, W., Jia, J.:Rotational motion deblurring of a rigid object from a single image. In: ICCV 2007. IEEE 11th International Conference on Computer Vision, 2007, IEEE, pp. 1–8Google Scholar
  26. 26.
    Kim, T.H., Lee, K.M.: Segmentation-free dynamic scene deblurring. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 2766–2773Google Scholar
  27. 27.
    Kim, T., Ahn, B., Lee, K.: Dynamic scene deblurring. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3160–3167 (2013)Google Scholar
  28. 28.
    Hyun Kim, T., Mu Lee, K.: Generalized video deblurring for dynamic scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5426–5434 (2015)Google Scholar
  29. 29.
    Javaran, T.A., Hassanpour, H., Abolghasemi, V.: A noise-immune no-reference metric for estimating blurriness value of an image. Signal Process Image Commun (2016). doi: 10.1016/j.image.2016.06.009 Google Scholar
  30. 30.
    Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 228–242 (2008)CrossRefGoogle Scholar
  31. 31.
    Pan, J., Hu, Z., Su, Z., Lee, H.-Y., Yang, M.-H.: Soft-segmentation guided object motion deblurring. In: Computer Vision and Pattern Recognition, 2016. IEEE Conference on CVPR 2016Google Scholar
  32. 32.
    Javaran, T. A., Hassanpour, H., Abolghasemi, V.: Automatic estimation and segmentation of partial blur in natural images. Vis. Comput. 33, 151 (2017). doi: 10.1007/s00371-015-1166-z
  33. 33.
    Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding blind deconvolution algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2354–2367 (2011)CrossRefGoogle Scholar
  34. 34.
    Joshi, N., Zitnick, C. L., Szeliski, R., Kriegman, D. J. Image deblurring and denoising using color priors. In: Computer Vision and Pattern Recognition, 2009. IEEE Conference on CVPR 2009, IEEE, pp. 1550–1557 (2009)Google Scholar
  35. 35.
    Osher, S., Rudin, L.I.: Feature-oriented image enhancement using shock filters. SIAM J. Numer. Anal. 27(4), 919–940 (1990)CrossRefMATHGoogle Scholar
  36. 36.
    S. Roth, M. J. Black, Fields of experts: A framework for learning image priors. In: Computer Vision and Pattern Recognition, 2005. IEEE Computer Society Conference on CVPR 2005, vol. 2, IEEE, pp. 860–867 (2005)Google Scholar
  37. 37.
    Y. Weiss, W. T. Freeman, What makes a good model of natural images?. In: Computer Vision and Pattern Recognition, 2007. IEEE Conference on CVPR’07, IEEE, pp. 1–8 (2007)Google Scholar
  38. 38.
    Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Deconvolution Using Natural Image Priors. Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory (2007)Google Scholar
  39. 39.
    A. Levin, Y. Weiss, F. Durand, W. T. Freeman, Understanding and evaluating blind deconvolution algorithms. In: Computer Vision and Pattern Recognition, 2009. IEEE Conference on CVPR 2009, IEEE, pp. 1964–1971 (2009)Google Scholar
  40. 40.
    Geman, D., Reynolds, G.: Constrained restoration and the recovery of discontinuities. IEEE Trans. Pattern Anal. Mach. Intell. 3, 367–383 (1992)CrossRefGoogle Scholar
  41. 41.
    Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization. IEEE Trans. Image Process. 4(7), 932–946 (1995)CrossRefGoogle Scholar
  42. 42.
    Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imag. Sci. 1(3), 248–272 (2008)MathSciNetCrossRefMATHGoogle Scholar
  43. 43.
    D. Krishnan, R. Fergus, Fast image deconvolution using hyper-laplacian priors. In: Advances in Neural Information Processing Systems, pp. 1033–1041 (2009)Google Scholar
  44. 44.
    Javaran, T.A., Hassanpour, H., Abolghasemi, V.: Non-blind deconvolution for image deblurring using a regularization based on re-blurring process. Comput. Vis. Image Underst. 154, 16–34 (2017)CrossRefGoogle Scholar
  45. 45.
    Shao, W.-Z., Li, H.-B., Elad, M.: Bi-l 0-l 2-norm regularization for blind motion deblurring. J. Vis. Commun. Image Rep. 33, 42–59 (2015)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Taiebeh Askari Javaran
    • 1
  • Hamid Hassanpour
    • 1
  • Vahid Abolghasemi
    • 1
  1. 1.Image Processing and Data Mining (IPDM) Research Lab, Faculty of Computer Engineering and Information TechnologyShahrood University of TechnologyShahroodIran

Personalised recommendations