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Analysis of Motion Blur with a Flutter Shutter Camera for Non-linear Motion

  • Yuanyuan Ding
  • Scott McCloskey
  • Jingyi Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

Motion blurs confound many computer vision problems. The fluttered shutter (FS) camera [1] tackles the motion deblurring problem by emulating invertible broadband blur kernels. However, existing FS methods assume known constant velocity motions, e.g., via user specifications. In this paper, we extend the FS technique to general 1D motions and develop an automatic motion-from-blur framework by analyzing the image statistics under the FS.

We first introduce a fluttered-shutter point-spread-function (FS-PSF) to uniformly model the blur kernel under general motions. We show that many commonly used motions have closed-form FS-PSFs. To recover the FS-PSF from the blurred image, we present a new method by analyzing image power spectrum statistics. We show that the Modulation Transfer Function of the 1D FS-PSF is statistically correlated to the blurred image power spectrum along the motion direction. We then recover the FS-PSF by finding the motion parameters that maximize the correlation. We demonstrate our techniques on a variety of motions including constant velocity, constant acceleration, and harmonic rotation. Experimental results show that our method can automatically and accurately recover the motion from the blurs captured under the fluttered shutter.

Keywords

Motion Estimation Modulation Transfer Function Motion Blur Motion Estimation Algorithm 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.

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References

  1. 1.
    Raskar, R., Agrawal, A., Tumblin, J.: Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans. Graph. 25, 795–804 (2006)CrossRefGoogle Scholar
  2. 2.
    Yitzhaky, Y., Mor, I., Lantzman, A., Kopeika, N.S.: Direct method for restoration of motion-blurred images. Journal of the Optical Society of America A: Optics, Image Science, and Vision 15, 1512–1519 (1998)CrossRefGoogle Scholar
  3. 3.
    Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. In: NIPS, pp. 1–8 (2009)Google Scholar
  4. 4.
    Levin, A.: Blind motion deblurring using image statistics. In: NIPS, pp. 841–848 (2007)Google Scholar
  5. 5.
    Agrawal, A., Xu, Y.: Coded exposure deblurring: Optimized codes for psf estimation and invertibility. In: CVPR (2009)Google Scholar
  6. 6.
    van der Schaaf, A., van Hateren, J.H.: Modelling the power spectra of natural images: Statistics and information. Vision Research 36, 2759–2770 (1996)CrossRefGoogle Scholar
  7. 7.
    Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. 26, 1 (2007)zbMATHCrossRefGoogle Scholar
  8. 8.
    Ott, K.A., Krahmer, F., Lin, Y., McAdoo, B., Wang, J., Widemann, D., Wohlberg, B.: Blind image deconvolution: Motion blur estimation. In: Technical Report for the Mathematical Modeling in Industry X Workshop (2006)Google Scholar
  9. 9.
    Moghaddam, M.E., Jamzad, M.: Fining point spread function of motion blur using radon transformation and modeling the motion length. In: ISSPIT (2004)Google Scholar
  10. 10.
    Ji, H., Liu, C.: Motion blur identification from image gradients. In: CVPR (2008)Google Scholar
  11. 11.
    Dai, S., Wu, Y.: Motion from blur. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)Google Scholar
  12. 12.
    Haykin, S.: Blind Deconvolution. Prentice-Hall, Englewood Cliffs (1994)Google Scholar
  13. 13.
    Jia, J.: Single image motion deblurring using transparency. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’07, pp. 1–8 (2007)Google Scholar
  14. 14.
    Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. In: SIGGRAPH ’08, pp. 1–10 (2008)Google Scholar
  15. 15.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. In: SIGGRAPH ’06, pp. 787–794 (2006)Google Scholar
  16. 16.
    Shan, Q., Xiong, W., Jia, J.: Rotational motion deblurring of a rigid object from a single image. In: ICCV 2007, pp. 1–8 (2007)Google Scholar
  17. 17.
    Agrawal, A., Xu, Y., Raskar, R.: Invertible motion blur in video. ACM Trans. Graph. 28, 1–8 (2009)CrossRefGoogle Scholar
  18. 18.
    Ben-Ezra, M., Nayar, S.K.: Motion-based motion deblurring. IEEE Trans. Pattern Anal. Mach. Intell. 26, 689–698 (2004)CrossRefGoogle Scholar
  19. 19.
    Li, F., Yu, J., Chai, J.: A hybrid camera for motion deblurring and depth map super-resolution. In: CVPR, pp. 1–8 (2008)Google Scholar
  20. 20.
    Tai, Y.W., Du, H., Brown, M.S., Lin, S.: Image/video deblurring using a hybrid camera. In: CVPR, pp. 1–8 (2008)Google Scholar
  21. 21.
    Levin, A., Fergus, R., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. In: ACM SIGGRAPH (2007)Google Scholar
  22. 22.
    Joshi, N., Zitnick, C., Szeliski, R., Kriegman, D.: Image deblurring and denoising using color priors, pp. 1550–1557 (2009)Google Scholar
  23. 23.
    Favaro, P., Soatto, S.: Learning shape from defocus. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 735–745. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  24. 24.
    Martin, D.R., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Technical Report UCB/CSD-01-1133, EECS Department, University of California, Berkeley (2001)Google Scholar
  25. 25.
    Toft, P.: The Radon Transform — Theory and Implementation. PhD thesis, Electronics Institute, Technical University of Denmark, Lyngby, Denmark (1996)Google Scholar
  26. 26.
    Oliveira, J.P., Figueiredo, M.A., Bioucas-Dias, J.M.: Blind estimation of motion blur parameters for image deconvolution. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 604–611. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  27. 27.
    Lucy, L.B.: An iterative technique for the rectification of observed distributions. Astron. Journal 79, 745 (1974)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yuanyuan Ding
    • 1
  • Scott McCloskey
    • 2
  • Jingyi Yu
    • 1
  1. 1.University of DelawareNewarkUSA
  2. 2.Honeywell LabsGolden ValleyUSA

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