Article

Removing camera shake from a single photograph

Authors:

Aaron Hertzmann,

William T. FreemanAuthors Info & Claims

SIGGRAPH '06: ACM SIGGRAPH 2006 Papers

Pages 787 - 794

https://doi.org/10.1145/1179352.1141956

Published: 01 July 2006 Publication History

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.

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References

[1]

Apostoloff, N., and Fitzgibbon, A. 2005. Bayesian video matting using learnt image priors. In Conf. on Computer Vision and Pattern Recognition, 407--414.

[2]

Bascle, B., Blake, A., and Zisserman, A. 1996. Motion Deblurring and Super-resolution from an Image Sequence. In ECCV(2), 573--582.

Digital Library

[3]

Ben-Ezra, M., and Nayar, S. K. 2004. Motion-Based Motion Deblurring. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 6, 689--698.

Digital Library

[4]

Biggs, D., and Andrews, M. 1997. Acceleration of iterative image restoration algorithms. Applied Optics 36, 8, 1766--1775.

[5]

Canon Inc., 2006. What is optical image stabilizer? http://www.canon.com/bctv/faq/optis.html.

[6]

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.

[7]

Field, D. 1994. What is the goal of sensory coding? Neural Computation 6, 559--601.

Digital Library

[8]

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.

Digital Library

[9]

Gull, S. 1998. Bayesian inductive inference and maximum entropy. In Maximum Entropy and Bayesian Methods, J. Skilling, Ed. Kluwer, 54--71.

[10]

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.

[11]

Jansson, P. A. 1997. Deconvolution of Images and Spectra. Academic Press.

Digital Library

[12]

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.

Digital Library

[13]

Kundur, D., and Hatzinakos, D. 1996. Blind image deconvolution. IEEE Signal Processing Magazine 13, 3 (May), 43--64.

[14]

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.

[15]

Levin, A., Zomet, A., and Weiss, Y. 2003. Learning How to Inpaint from Global Image Statistics. In ICCV, 305--312.

Digital Library

[16]

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.

Digital Library

[17]

Lucy, L. 1974. Bayesian-based iterative method of image restoration. Journal of Astronomy 79, 745--754.

[18]

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.

[19]

Miskin, J., 2000. Train ensemble library. http://www.inference.phy.cam.ac.uk/jwm1003/train_ensemble.tar.gz.

[20]

Miskin, J. W. 2000. Ensemble Learning for Independent Component Analysis. PhD thesis, University of Cambridge.

[21]

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.

Digital Library

[22]

Rav-Acha, A., and Peleg, S. 2005. Two motion-blurred images are better than one. Pattern Recognition Letters, 311--317.

Digital Library

[23]

Richardson, W. 1972. Bayesian-based iterative method of image restoration. Journal of the Optical Society of America A 62, 55--59.

[24]

Roth, S., and Black, M. J. 2005. Fields of Experts: A Framework for Learning Image Priors. In CVPR, vol. 2, 860--867.

Digital Library

[25]

Simoncelli, E. P. 2005. Statistical modeling of photographic images. In Handbook of Image and Video Processing, A. Bovik, Ed. ch. 4.

[26]

Tappen, M. F., Russell, B. C., and Freeman, W. T. 2003. Exploiting the sparse derivative prior for super-resolution and image demosaicing. In SCTV.

[27]

Tsumuraya, F., Miura, N., and Baba, N. 1994. Iterative blind deconvolution method using Lucy's algorithm. Astron. Astrophys. 282, 2 (Feb), 699--708.

[28]

Weiss, Y. 2001. Deriving intrinsic images from image sequences. In ICCV, 68--75.

[29]

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.

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Index Terms

Removing camera shake from a single photograph
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics
    1. Image manipulation
2. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM Conferences

SIGGRAPH '06: ACM SIGGRAPH 2006 Papers

July 2006

742 pages

ISBN:1595933646

DOI:10.1145/1179352

Conference Chair:
John Finnegan
Purdue University
,
Program Chair:
Julie Dorsey
Yale University

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 July 2006

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SIGGRAPH06: Special Interest Group on Computer Graphics and Interactive Techniques Conference

July 30 - August 3, 2006

Massachusetts, Boston

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SIGGRAPH '06 Paper Acceptance Rate 86 of 474 submissions, 18%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Gong HFeng YZhang ZHou XLiu JHuang SLiu HLarson K(2024)A dataset and model for realistic license plate deblurringProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/86(776-784)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/86
Li HShi HGao XLarson K(2024)A coarse-to-fine fusion network for event-based image deblurringProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/108(974-982)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/108
Gossard AWeiss P(2024)Training Adaptive Reconstruction Networks for Blind Inverse ProblemsSIAM Journal on Imaging Sciences10.1137/23M154562817:2(1314-1346)Online publication date: 21-Jun-2024
https://doi.org/10.1137/23M1545628
Kim JKim MKim IHan GJong MRi G(2024)Image Motion Blur Removal Algorithm Based on Generative Adversarial NetworkProgramming and Computing Software10.1134/S036176882470020850:5(403-415)Online publication date: 1-Oct-2024
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Lee HKang KLee HBaek SCho S(2024)UGPNet: Universal Generative Prior for Image Restoration2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00162(1587-1597)Online publication date: 3-Jan-2024
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Liu CZhao JZhu CXia XLong H(2024)MECFNet: Reconstruct Sharp Image for UAV-Based Crack DetectionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.340175425:10(15016-15028)Online publication date: Oct-2024
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Ren WWu LYan YXu SHuang FCao X(2024)INformer: Inertial-Based Fusion Transformer for Camera Shake DeblurringIEEE Transactions on Image Processing10.1109/TIP.2024.346196733(6045-6056)Online publication date: 2024
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Lin SZhang XYang LYu LZhou BLuo XWang WPan J(2024)Neuromorphic Synergy for Video BinarizationIEEE Transactions on Image Processing10.1109/TIP.2024.336452933(1403-1418)Online publication date: 2024
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Zhu QZheng NHuang JZhou MZhang JZhao F(2024)Learning Spatio-Temporal Sharpness Map for Video DeblurringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.332540834:5(3957-3970)Online publication date: May-2024
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