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Detection and restoration of multi-directional motion blurred objects

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Abstract

In this paper, an object blur detection and deblurring technique is proposed to restore multi-directional motion blurred objects in a single image. We have proposed local blur angle detection method based on Radon transform (RT) and Laplacian of Gaussian (LoG). While capturing the images, motion blur occurs mainly due to either movement of the objects or movement of the camera. Here, we have focused to restore the objects which has been blurred by motion of the objects. The estimation of likely blur direction is calculated in the blurred image using RT and gradient operators. To detect blur angle locally at each pixel, the new local blur angle estimator using RT and LoG has been developed. Numerical experiments have been carried out for the proposed method, and the results are compared with the state-of-the-art methods.

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References

  1. Gonzalez, R.C., Wintz, P.: Digital Image Processing. Addison-Wesley, New York (1987)

    MATH  Google Scholar 

  2. Pratt, W.K.: Digital Image Processing. Wiley, New York (1991)

    MATH  Google Scholar 

  3. Jain, A.K.: Fundamentals in Digital Image Processing. Prentice-Hall, Englewood Cliffs (1989)

    MATH  Google Scholar 

  4. Lagendijk, R.L., Biemond, J.: Iterative Identification and Restoration of Images. Kluwer, Boston (1991)

    Book  MATH  Google Scholar 

  5. Oliveira, J.P., Figueiredo, M.A.T., Bioucas-Dias, J.M.: Blind estimation of motion blur parameters for image deconvolution. In: Iberian Conference on Pattern Recognition and Image Analysis-IbPRIA, pp. 604–611 (2007)

  6. Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)

    Article  MATH  Google Scholar 

  7. Krahmer, F., Lin, Y., McAdoo, B., Ott, K., Wang, J., Widemannk, D.: Blind image deconvolution: motion blur estimation. Technical report, Institute for Mathematics and its Applications, University of Minnesota, Minneapolis, Minnesota (2006)

  8. Oliveira, J.P., Figueiredo, M.A.T., Bioucas-Dias, J.M.: Parametric blur estimation for blind restoration of natural images: linear motion and out-of-focus. IEEE Trans. Image Process. 23(1), 466–477 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Almeida, M.S.C., Almeida, L.B.: Blind and semi-blind deblurring of natural images. IEEE Trans. Image Process. 19(1), 36–52 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  10. 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  MATH  Google Scholar 

  11. Cai, J.F., Osher, S., Shen, Z.: Linearized Bregman iterations for frame-based image deblurring. SIAM J. Imaging Sci. 2(1), 226–252 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  12. Cai, J.F., Osher, S., Shen, Z.: Split Bregman method and frame based image restoration. Multiscale Model. Simul. 8(2), 337–369 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  13. Cai, J.F., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: CVPR (2009)

  14. 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)

  15. Ji, H., Liu, C.: Motion blur identification from image gradients. In: CVPR, pp. 1–8 (2008)

  16. Richardson, W.H.: Bayesian-based iterative method of image restoration. J. Opt. Soc. Am. 62(1), 55–59 (1972)

    Article  Google Scholar 

  17. Sun, H., Desvignes, M., Yan, Y., Liu, W.: Motion blur parameters identification from radon transform image gradients. In: Proceedings of the Conference on Industrial Electronics (2009)

  18. Tai, Y.W., Tan, P., Brown, M.S.: Richardson–Lucy deblurring for scenes under a projective motion path. IEEE TPAMI 33(8), 1603–1618 (2011)

    Article  Google Scholar 

  19. Sakano, M., Suetake, N., Uchino, E.: Robust identification of motion blur parameters by using angles of gradient vectors. In: Proceedings of ISPACS, pp. 522–525 (2006)

  20. Gupta, A., Joshi, N., Zitnick, C.L., Cohen, M.F., Curless, B.: Single image deblurring using motion density functions. In: ECCV, pp. 171–184 (2010)

  21. Tai, Y.W., Du, H., Brown, M., Lin, S.: Image/video deblurring using a hybrid camera. In: Proceedings of IEEE CVPR (2008)

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

  23. Oyamada, Y., Asai, H., Saito, H.: Blind deconvolution for a curved motion based on cepstral analysis. IPSJ Trans. Comput. Vis. Appl. (CVA) 3, 32–43 (2011)

    Article  Google Scholar 

  24. Kheradmand, A., Milanfar, Peyman: A general framework for regularized, similarity-based image restoration. IEEE Trans. Image Process. 23(12), 5136–5151 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  25. Ji, H., Wang, K.: A two-stage approach to blind spatially-varying motion deblurring. In: CVPR (2012)

  26. Levin, A.: Blind motion deblurring using image statistics. In: Proceedings of Advances in NIPS, pp. 841–848 (2006)

  27. Kim, T.H., Lee, K.M.: Segmentation-free dynamic scene deblurring. In: CVPR, pp. 2766–2773 (2014)

  28. Harmeling, S., Michael, H., Schoelkopf, B.: Space-variant single-image blind deconvolution for removing camera shake. In: NIPS (2010)

  29. Kim, T.H., Nah, S., Lee, K.M.: Dynamic scene deblurring using a locally adaptive linear blur model. In: Computer Vision (2016)

  30. Almeida, M.S.C., Almeida, L.B.: Blind deblurring of foreground background images. In: International Conference on Image Processing (ICIP), pp. 1301–1304 (2009)

  31. Almeida, M.S.C., Figueiredo, M.A.: Parameter estimation for blind and nonblind deblurring using residual whiteness measures. IEEE Trans. Image Process. 22(7), 2751–2763 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  32. Pan, J., Hu, Z., Su, Z., Lee, H.-Y., Yang, M.-H.: Soft-segmentation guided object motion deblurring (2015)

  33. Mignotte, M.: An adaptive segmentation-based regularization term for image restoration. In: IEEE International Conference on Image Processing, ICIP, vol. 1, p. I9014 (2005)

  34. Zhang, X., Burger, M., Bresson, X., Osher, S.: Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Imaging Sci. 3(3), 253–276 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  35. Couzinie-Devy, F., Sun, J., Alahari, K., Ponce, J.: Learning to estimate and remove non-uniform image blur. In: CVPR (2013)

  36. Favaro, P., Soatto, S.: A variational approach to scene reconstruction and image segmentation from motion-blur cues. In: CVPR (2004)

  37. Kang, S., Choung, Y., Paik, J.: Segmentation-based image restoration for multiple moving objects with different motions. In: ICIP, pp. 376–380 (1999)

  38. Kang, S., Min, J., Paik, J.: Segmentation-based spatially adaptive motion blur removal and its application to surveillance systems. In: ICIP, pp. 245–248 (2001)

  39. McGuire, M., Hennessy, P, Bukowski, M., Osman, B.: A reconstruction filter for plausible motion blur (2012)

  40. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Commun. Pure Appl. Math. 42, 577–685 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  41. Osher, Stanley, Sethian, James A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  42. Kass, K., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)

    Article  MATH  Google Scholar 

  43. Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  44. Bracewell, R.: Two-Dimensional Imaging. Prentice-Hall, Upper Saddle River (1995)

    MATH  Google Scholar 

  45. Moghaddam, M., Jamzad, M.: Motion blur identification in noisy motion blur identification in noisy images using fuzzy sets, In: Proceedings of the 5th IEEE International Symposium on Signal Processing and Information Technology, pp. 862–866 (2005)

  46. McCormick, W.P., Lyons, N.I., Hutcheson, K.: Distributional properties of Jaccard’s index of similarity. Commun. Stat. Theory Methods 21, 51–68 (1992)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors would like to thank to Defence Institute of Advanced Technology, Pune, and Centre for Airborne Systems, Bangalore, for providing infrastructure to carry out the research work.

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Authors and Affiliations

  1. Department of Applied Mathematics, Defence Institute of Advanced Technology (DIAT), Pune, 411025, India

    B. R. Kapuriya & Debasish Pradhan

  2. Centre for Airborne Systems (CABS), Bangalore, 560037, India

    Reena Sharma

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  1. B. R. Kapuriya
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  2. Debasish Pradhan
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  3. Reena Sharma
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Correspondence to Debasish Pradhan.

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Kapuriya, B.R., Pradhan, D. & Sharma, R. Detection and restoration of multi-directional motion blurred objects. SIViP 13, 1001–1010 (2019). https://doi.org/10.1007/s11760-019-01438-z

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  • DOI: https://doi.org/10.1007/s11760-019-01438-z

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