Abstract
We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Bishop, T.E., Babacan, S.D., Amizic, B., Chan, T., Molina, R., Katsaggelos, A.K.: Blind image deconvolution: problem formulation and existing approaches. In: Blind Image Deconvolution: Theory and Applications. CRC Press (2007)
Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25, 787–794 (2006)
Joshi, N., Zitnick, C., Szeliski, R., Kriegman, D.: Image deblurring and denoising using color priors. In: CVPR (2009)
Jia, J.: Single image motion deblurring using transparency. In: CVPR (2007)
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. (SIGGRAPH) (2008)
Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. (SIGGRAPH ASIA) 28 (2009)
Xu, L., Jia, J.: Two-Phase Kernel Estimation for Robust Motion Deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR, pp. 1964–1971 (2009)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: CVPR, pp. 2657–2664 (2011)
Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: CVPR (2011)
Miskin, J.W., MacKay, D.J.C.: Ensemble learning for blind image separation and deconvolution. In: Advances in Independent Component Analysis. Springer (2000)
Whyte, O., Sivic, J., Zisserman, A., Ponce, J.: Non-uniform deblurring for shaken images. In: ICCV (2010)
Hirsch, M., Schuler, C.J., Harmeling, S., Schölkopf, B.: Fast removal of non-uniform camera shake. In: ICCV (2011)
Palmer, J.A., Kreutz-Delgado, K., Makeig, S.: Strong Sub- and Super-Gaussianity. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds.) LVA/ICA 2010. LNCS, vol. 6365, pp. 303–310. Springer, Heidelberg (2010)
Rockafellar, R.T.: Convex analysis. Princeton University Press (1996)
Black, M.J., Rangarajan, A.: On the unification of line processes, outlier rejection, and robust statistics with applications in early vision. IJCV 19, 57–91 (1996)
Andrews, D.F., Mallows, C.L.: Scale mixtures of normal distributions. Journal of the Royal Statistical Society. Series B (Methodological) 36, 99–102 (1974)
Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26 (2007)
Bishop, C.: Pattern Recognition and Machine Learning. Springer (2006)
Likas, A.C., Galatsanos, N.P.: A variational approach for Bayesian blind image deconvolution. IEEE Trans. on Signal Proc. 52, 2222–2233 (2004)
Babacan, S.D., Molina, R., Katsaggelos, A.K.: Variational Bayesian super resolution. IEEE Trans. Image Proc. 20, 984–999 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Babacan, S.D., Molina, R., Do, M.N., Katsaggelos, A.K. (2012). Bayesian Blind Deconvolution with General Sparse Image Priors. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33783-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-33783-3_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33782-6
Online ISBN: 978-3-642-33783-3
eBook Packages: Computer ScienceComputer Science (R0)