Abstract
In this paper, a novel approach is proposed to remove the motion blur from a video, which is degraded and distorted by fast camera motion. Our approach is based on the image statistics rather than the traditional motion estimation. The image statistics has been successfully applied for blind motion deblurring for a single image by Fergus et al [3] and Levin [10]. Here a three-stage method is used to deal with the video. First, the “unblurred” frames in the video can be found based on the image statistics. Then the blur functions can be obtained by comparing the blurred frames with the unblurred ones. Finally a standard deconvolution algorithm is used to reconstruct the video. Our experiments show that our algorithms are efficient.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ben-Ezra, M., Nayar, S.K.: Motion-based motion deblurring. IEEE Trans. on PAMI 26, 689–698 (2004)
Biggs, D., Andrews, M.: Acceleration of iterative image restoration algorithms. Applied Optics. 36, 1766–1775 (1997)
Fergus, R., et al.: Removing camera shake from a single photograph. In: SIGGRAPH (2006)
Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MA (1989)
Herrera, F., Lozano, M.: Gradual distributed real-coded genetic algorithms. IEEE Trans. on Evolutionary Computation 4, 43–63 (2000)
Irani, M., Peleg, S.: Improving resolution by image registration. Graphical Models and Image Processing. 53, 231–239 (1991)
Jansson, P.A.: Deconvolution of images and spectra. Academic Press, London (1997)
Jia, J., Tang, C.: Image registration with global and local luminance alignment. In: ICCV (2003)
Koza, J.: Genetic programming: on the programming of computers by means of natural selection. MIT Press, MA (1992)
Levin, A.: Blind motion deblurring using image statistics. In: NIPS (2006)
Lucy, L.: Bayesian-based iterative method of image restoration. Journal of Astronomy 79, 745–754 (1974)
Mitchell, M.: An introduction to genetic algorithms. MIT Press, Cambridge, MA (1996)
Raj, A., Zabih, R.: A graph cut algorithm for generalized image deconvolution. In: ICCV (2005)
Rasheed, Z., Shah, M.: Scene detection in Hollywood movies and TV shows. In: CVPR (2003)
Raskar, R., Agrawal, A., Tumblin, J.: Coded exposure photography: motion deblurring using fluttered shutter. In: SIGGRAPH (2006)
Rav-Acha, A., Peleg, S.: Two motion-blurred images are better than one. Pattern Recognition Letters, 311–317 (2005)
Richardson, W.: Bayesian-based iterative method of image restoration. Journal of the Optical Society of America. 62, 55–59 (1972)
Shah, N.R., Zakhor, A.: Resolution enhancement of color video sequences. IEEE Trans. on IP 8, 879–885 (1999)
Simoncelli, E.P.: Statistical modeling of photographic images. In: Handbook of Image and Video Processing (2005)
Tom, B.C., Katsaggelos, A.K.: Resolution enhancement of video sequence using motion compensation. In: ICIP (1996)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ren, C., Chen, W., Shen, If. (2007). Three-Stage Motion Deblurring from a Video. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-540-76390-1_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76389-5
Online ISBN: 978-3-540-76390-1
eBook Packages: Computer ScienceComputer Science (R0)