Abstract
Motion blur is caused by the camera shake during the exposure in which the blur kernel describes the trace of shaking. Based on this generating process of the kernel , we observed that the distribution of the kernel obeys super-sparsity, as the natural images. Recent works mostly exploit various kinds of priors in their models, but focus on the the speed or a close-form formulation for convenience of mathematical calculation ignoring the intrinsic feature of the kernels and images. In this paper we propose a new model with super-sparse prior for the deblurring problem from one single image. Since the close-form formulation of this model doesn’t exist, we use a look-up table trick to approximate the solution. Qualitative and quantitative evaluation demonstrate that our model with super-sparse prior can produce stable and high-quality results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fergus, R., Singh, B., Hertzmann, A., Rowels, S.T., Freeman, W.T.: Removing camera shake from a single photograph. In: ACM SIGGRAPH, pp. 787–794 (2006)
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. In: ACM SIGGRAPH, pp. 73:1–73:10 (2008)
Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithm. In: CVPR, pp. 1964–1971 (2009)
Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28 (2009)
Joshi, N., Szeliski, R., Kriegman, D.J.: PSF estimation using sharp edge prediction. In: CVPR (2007)
Kundur, D., Hatzinakos, D.: Blind image deconvolution. SPMag 13(3), 43–64 (1996)
You, Y.-L., Kaveh, M.: Blind image restoration by anisotropic regularization. IEEE Transactions on Image Processing 8, 396–407 (1999)
Yitzhaky, Y., Mor, I.: Direct method for restoration of motion-blurred images. Journal of Opt. Soc. Am. A 15(6), 1512–1519 (1998)
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)
Hu, Z., Yang, M.-H.: Good regions to deblur. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 59–72. Springer, Heidelberg (2012)
Bar, L., Sochen, N., Kiryati, N.: Image deblurring in the presence of salt-and-pepper noise. In: Kimmel, R., Sochen, N.A., Weickert, J. (eds.) Scale-Space 2005. LNCS, vol. 3459, pp. 107–118. Springer, Heidelberg (2005)
Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Image Sciences 1(3), 248–272 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, J., Zhao, H., Zhang, K., Zhang, L. (2013). Motion Deblurring Using Super-Sparsity. In: Lee, M., Hirose, A., Hou, ZG., Kil, R.M. (eds) Neural Information Processing. ICONIP 2013. Lecture Notes in Computer Science, vol 8228. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42051-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-42051-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42050-4
Online ISBN: 978-3-642-42051-1
eBook Packages: Computer ScienceComputer Science (R0)