“Image deblurring using inertial measurement sensors” by Joshi, Kang, Zitnick and Szeliski

  • ©Neel Joshi, Sing Bing Kang, Lawrence C. Zitnick, and Richard Szeliski

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Title:

    Image deblurring using inertial measurement sensors

Presenter(s)/Author(s):



Abstract:


    We present a deblurring algorithm that uses a hardware attachment coupled with a natural image prior to deblur images from consumer cameras. Our approach uses a combination of inexpensive gyroscopes and accelerometers in an energy optimization framework to estimate a blur function from the camera’s acceleration and angular velocity during an exposure. We solve for the camera motion at a high sampling rate during an exposure and infer the latent image using a joint optimization. Our method is completely automatic, handles per-pixel, spatially-varying blur, and out-performs the current leading image-based methods. Our experiments show that it handles large kernels — up to at least 100 pixels, with a typical size of 30 pixels. We also present a method to perform “ground-truth” measurements of camera motion blur. We use this method to validate our hardware and deconvolution approach. To the best of our knowledge, this is the first work that uses 6 DOF inertial sensors for dense, per-pixel spatially-varying image deblurring and the first work to gather dense ground-truth measurements for camera-shake blur.

References:


    1. Bascle, B., Blake, A., and Zisserman, A. 1996. Motion deblurring and super-resolution from an image sequence. In ECCV ’96: Proceedings of the 4th European Conference on Computer Vision-Volume II, Springer-Verlag, London, UK, 573–582. Google ScholarDigital Library
    2. Ben-Ezra, M., and Nayar, S. K. 2004. Motion-based motion deblurring. IEEE Trans. Pattern Anal. Mach. Intell. 26, 6, 689–698. Google ScholarDigital Library
    3. Canon, L. G. 1993. EF LENS WORK III, The Eyes of EOS. Canon Inc.Google Scholar
    4. Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., and Freeman, W. T. 2006. Removing camera shake from a single photograph. ACM Trans. Graph. 25 (July), 787–794. Google ScholarDigital Library
    5. Joshi, N., Szeliski, R., and Kriegman, D. J. 2008. Psf estimation using sharp edge prediction. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 1–8.Google Scholar
    6. Kundur, D., and Hatzinakos, D. 1996. Blind image deconvolution. Signal Processing Magazine, IEEE 13, 3, 43–64.Google ScholarCross Ref
    7. Levin, A., Fergus, R., Durand, F., and Freeman, W. T. 2007. Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26 (July), Article 70. Google ScholarDigital Library
    8. Levin, A., Sand, P., Cho, T. S., Durand, F., and Freeman, W. T. 2008. Motion-invariant photography. ACM Trans. Graph. 27 (August), 71:1–71:9. Google ScholarDigital Library
    9. Levin, A., Weiss, Y., Durand, F., and Freeman, W. 2009. Understanding and evaluating blind deconvolution algorithms. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE Computer Society, 1964–1971.Google Scholar
    10. Park, S.-Y., Park, E.-S., and Kim, H.-I. 2008. Image deblurring using vibration information from 3-axis accelerometer. Journal of the Institute of Electronics Engineers of Korea. SC, System and control 45, 3, 1–11.Google Scholar
    11. Raskar, R., Agrawal, A., and Tumblin, J. 2006. Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans. Graph. 25 (July), 795–804. Google ScholarDigital Library
    12. Richardson, W. H. 1972. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America (1917–1983) 62, 55–59.Google Scholar
    13. Shan, Q., Jia, J., and Agarwala, A. 2008. High-quality motion deblurring from a single image. ACM Trans. Graph. 27 (August), 73:1–73:10. Google ScholarDigital Library
    14. Stewart, C. V. 1999. Robust parameter estimation in computer vision. SIAM Reviews 41, 3 (September), 513–537. Google ScholarDigital Library
    15. Tai, Y.-W., Du, H., Brown, M. S., and Lin, S. 2008. Image/video deblurring using a hybrid camera. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, 1–8.Google Scholar
    16. Yuan, L., Sun, J., Quan, L., and Shum, H.-Y. 2007. Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. 26 (July), Article 1. Google ScholarDigital Library


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