ABSTRACT
Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequency-domain constraints on images, or overly simplified parametric forms for the motion path during camera shake. Real camera motions can follow convoluted paths, and a spatial domain prior can better maintain visually salient image characteristics. We introduce a method to remove the effects of camera shake from seriously blurred images. The method assumes a uniform camera blur over the image and negligible in-plane camera rotation. In order to estimate the blur from the camera shake, the user must specify an image region without saturation effects. We show results for a variety of digital photographs taken from personal photo collections.
Supplemental Material
- Apostoloff, N., and Fitzgibbon, A. 2005. Bayesian video matting using learnt image priors. In Conf. on Computer Vision and Pattern Recognition, 407--414.Google Scholar
- Bascle, B., Blake, A., and Zisserman, A. 1996. Motion Deblurring and Super-resolution from an Image Sequence. In ECCV(2), 573--582. Google ScholarDigital Library
- Ben-Ezra, M., and Nayar, S. K. 2004. Motion-Based Motion Deblurring. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 6, 689--698. Google ScholarDigital Library
- Biggs, D., and Andrews, M. 1997. Acceleration of iterative image restoration algorithms. Applied Optics 36, 8, 1766--1775.Google ScholarCross Ref
- Canon Inc., 2006. What is optical image stabilizer? http://www.canon.com/bctv/faq/optis.html.Google Scholar
- Caron, J., Namazi, N., and Rollins, C. 2002. Noniterative blind data restoration by use of an extracted filter function. Applied Optics 41, 32 (November), 68--84.Google ScholarCross Ref
- Field, D. 1994. What is the goal of sensory coding? Neural Computation 6, 559--601. Google ScholarDigital Library
- Geman, D., and Reynolds, G. 1992. Constrained restoration and the recovery of discontinuities. IEEE Trans. on Pattern Analysis and Machine Intelligence 14, 3, 367--383. Google ScholarDigital Library
- Gull, S. 1998. Bayesian inductive inference and maximum entropy. In Maximum Entropy and Bayesian Methods, J. Skilling, Ed. Kluwer, 54--71.Google Scholar
- Jalobeanu, A., Blanc-Fraud, L., and Zerubia, J. 2002. Estimation of blur and noise parameters in remote sensing. In Proc. of Int. Conf. on Acoustics, Speech and Signal Processing.Google Scholar
- Jansson, P. A. 1997. Deconvolution of Images and Spectra. Academic Press. Google ScholarDigital Library
- Jordan, M., Ghahramani, Z., Jaakkola, T., and Saul, L. 1999. An introduction to variational methods for graphical models. In Machine Learning, vol. 37, 183--233. Google ScholarDigital Library
- Kundur, D., and Hatzinakos, D. 1996. Blind image deconvolution. IEEE Signal Processing Magazine 13, 3 (May), 43--64.Google Scholar
- Levin, A., and Weiss, Y. 2004. User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior. In ICCV, vol. 1, 602--613.Google Scholar
- Levin, A., Zomet, A., and Weiss, Y. 2003. Learning How to Inpaint from Global Image Statistics. In ICCV, 305--312. Google ScholarDigital Library
- Liu, X., and Gamal, A. 2001. Simultaneous image formation and motion blur restoration via multiple capture. In Proc. Int. Conf. Acoustics, Speech, Signal Processing, vol. 3, 1841--1844. Google ScholarDigital Library
- Lucy, L. 1974. Bayesian-based iterative method of image restoration. Journal of Astronomy 79, 745--754.Google ScholarCross Ref
- Miskin, J., and MacKay, D. J. C. 2000. Ensemble Learning for Blind Image Separation and Deconvolution. In Adv. in Independent Component Analysis, M. Girolani, Ed. Springer-Verlag.Google Scholar
- Miskin, J., 2000. Train ensemble library. http://www.inference.phy.cam.ac.uk/jwm1003/train_ensemble.tar.gz.Google Scholar
- Miskin, J. W. 2000. Ensemble Learning for Independent Component Analysis. PhD thesis, University of Cambridge.Google Scholar
- Neelamani, R., Choi, H., and Baraniuk, R. 2004. Forward: Fourier-wavelet regularized deconvolution for ill-conditioned systems. IEEE Trans. on Signal Processing 52 (Feburary), 418--433. Google ScholarDigital Library
- Rav-Acha, A., and Peleg, S. 2005. Two motion-blurred images are better than one. Pattern Recognition Letters, 311--317. Google ScholarDigital Library
- Richardson, W. 1972. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America A 62, 55--59.Google ScholarCross Ref
- Roth, S., and Black, M. J. 2005. Fields of Experts: A Framework for Learning Image Priors. In CVPR, vol. 2, 860--867. Google ScholarDigital Library
- Simoncelli, E. P. 2005. Statistical modeling of photographic images. In Handbook of Image and Video Processing, A. Bovik, Ed. ch. 4.Google Scholar
- Tappen, M. F., Russell, B. C., and Freeman, W. T. 2003. Exploiting the sparse derivative prior for super-resolution and image demosaicing. In SCTV.Google Scholar
- Tsumuraya, F., Miura, N., and Baba, N. 1994. Iterative blind deconvolution method using Lucy's algorithm. Astron. Astrophys. 282, 2 (Feb), 699--708.Google Scholar
- Weiss, Y. 2001. Deriving intrinsic images from image sequences. In ICCV, 68--75.Google Scholar
- Zarowin, C. 1994. Robust, noniterative, and computationally efficient modification of van Cittert deconvolution optical figuring. Journal of the Optical Society of America A 11, 10 (October), 2571--83.Google ScholarCross Ref
Index Terms
- Removing camera shake from a single photograph
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