Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2012, Article ID 639824, 10 pages
http://dx.doi.org/10.1155/2012/639824
Research Article

Kernel Optimization for Blind Motion Deblurring with Image Edge Prior

College of Computing & Communication Engineering, Graduate University of Chinese Academy of Science, Beijing 100049, China

Received 10 January 2012; Accepted 20 February 2012

Academic Editor: Ming Li

Copyright © 2012 Jing Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P. C. Hansen, J. G. Nagy, and D. P. O'Leary, Deblurring Images: Matrices, Spectra, and Filtering, vol. 3, Society for Industrial and Applied Mathematics SIAM, Philadelphia, Pa, USA, 2006.
  2. M. Ben-Ezra and S. K. Nayar, “Motion-based motion deblurring,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 689–698, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Y. Chen, J. H. Zhang, Y. F. Li, and J. W. Zhang, “A hierarchical model incorporating segmented regions and pixel descriptors for video background subtraction,” IEEE Transactions on Industrial Informatics, vol. 8, no. 2, 2012. View at Google Scholar
  4. A. Rav-Acha and S. Peleg, “Two motion-blurred images are better than one,” Pattern Recognition Letters, vol. 26, no. 3, pp. 311–317, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Y. Chen, H. Tong, and C. Cattani, “Markov models for image labeling,” Mathematical Problems in Engineering, vol. 2012, Article ID 814356, 18 pages, 2012. View at Publisher · View at Google Scholar
  6. W. Li, J. Zhang, and Q. H. Dai, “Exploring aligned complementary image pair for blind motion deblurring,” in Proceedings of the IEEE International Conference Computer Vision and Pattern Recognition (CVPR '11), pp. 273–280.
  7. D. Kundur and D. Hatzinakos, “Blind image deconvolution,” IEEE Signal Processing Magazine, vol. 13, no. 3, pp. 43–64, 1996. View at Google Scholar · View at Scopus
  8. R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman, “Removing camera shake from a single photograph,” in Proceedings of the Annual Meeting of the Association for Computing Machinery's Special Interest Group on Graphics (SIGGRAPH '06), pp. 787–794, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Jia, “Single image motion deblurring using transparency,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'07), pp. 1–8, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. J. F. Cai, H. Ji, C. Liu, and Z. Shen, “Blind motion deblurring from a single image using sparse approximation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 104–111, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Joshi, R. Szeliski, and D. J. Kriegman, “PSF estimation using sharp edge prediction,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), June 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Xu and J. Jia, “Two-phase kernel estimation for robust motion deblurring,” Computer Vision-ECCV, vol. 6311, no. 1, pp. 157–170, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Y. Chen and Y. F. Li, “Determination of stripe edge blurring for depth sensing,” IEEE Sensors Journal, vol. 11, no. 2, Article ID 5585653, pp. 389–390, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 2nd edition, 2002.
  15. M. Li, C. Cattani, and S.-Y. Chen, “Viewing sea level by a one-dimensional random function with long memory,” Mathematical Problems in Engineering, vol. 2011, Article ID 654284, 13 pages, 2011. View at Publisher · View at Google Scholar
  16. L. Rudin and S. Osher, “Total variation based image restoration with free local constraints,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '94), pp. 31–35, Austin, Tex, USA, 1994.
  17. M. Li and W. Zhao, “Representation of a stochastic traffic bound,” IEEE Transactions on Parallel and Distributed Systems, vol. 21, no. 9, Article ID 5342414, pp. 1368–1372, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. C. R. Vogel and M. E. Oman, “Fast, robust total variation-based reconstruction of noisy, blurred images,” IEEE Transactions on Image Processing, vol. 7, no. 6, pp. 813–824, 1998. View at Google Scholar · View at Scopus
  19. Y. Wang, J. Yang, W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM Journal on Imaging Sciences, vol. 1, no. 3, pp. 248–272, 2008. View at Publisher · View at Google Scholar
  20. M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. Figueiredo, “Fast image recovery using variable splitting and constrained optimization,” IEEE Transactions on Image Processing, vol. 19, no. 9, Article ID 5445028, pp. 2345–2356, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. S. H. Chan, P. E. Gill, and T. Q. Nguyen, “An augmented lagrangian method for total variation image restoration,” IEEE Transactions on Image Processing, vol. 20, no. 11, pp. 3097–3111, 2011. View at Google Scholar
  22. V. Torre and T. A. Poggio, “On edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 2, pp. 147–163, 1986. View at Google Scholar · View at Scopus
  23. T.-H. H. Lee, “Edge detection analysis,” 2007, http://disp.ee.ntu.edu.tw/.
  24. S. Y. Chen, G. J. Luo, X. Li, S. M. Ji, and B. W. Zhang, “The specular exponent as a criterion for appearance quality assessment of pearl-like objects by artificial vision,” IEEE Transactions on Industrial Electronics, vol. 59, no. 99, 2012. View at Google Scholar
  25. J. Sun, Z. Xu, and H. Y. Shum, “Image super-resolution using gradient profile prior,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Y. Chen, H. Tong, Z. Wang, S. Liu, M. Li, and B. Zhang, “Improved generalized belief propagation for vision processing,” Mathematical Problems in Engineering, Article ID 416963, 12 pages, 2011. View at Publisher · View at Google Scholar
  27. S. Cho and S. Lee, “Fast motion deblurring,” in Proceedings of the Annual Meeting of the Association for Computing Machinery's Special Interest Group on Graphics, vol. 28, p. 145, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. D. Krishnan, T. Tay, and R. Fergus, “Blind deconvolution using a normalized sparsity measure,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 233–240, Providence, RI, USA, 2011.
  29. A. Levin, R. Fergus, F. Durand, and W. T. Freeman, “Image and depth from a conventional camera with a coded aperture,” in Proceedings of the 34th Annual Meeting of the Association for Computing Machinery's Special Interest Group on Graphics, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  30. T. Goldstein and S. Osher, “The split bregman algorithm for L1 regularized problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 323–343, 2009. View at Google Scholar
  31. S. Osher, M. Burger, D. Goldfarb, J. Xu, and W. Yin, “An iterative regularization method for total variation-based image restoration,” Multiscale Modeling and Simulation, vol. 4, no. 2, pp. 460–489, 2005. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Wang, K. Lu, and N. He, “Total variant image deblurring based on split bregman method,” Chinese Journal of Electronics. In press.
  33. W. S. Chen, P. C. Yuen, and X. H. Xie, “Kernel machine based rank-lifting regularized Discriminant analysis method for face recognition,” neurocomputing, vol. 74, no. 17, pp. 2953–2960, 2011. View at Google Scholar
  34. Z. Liao, S. Hu, M. Li, and W. Chen, “Noise estimation for single-slice sinogram of low-dose X-Ray computed tomography using homogenous patch,” Mathematical Problems in Engineering, vol. 2012, Article ID 696212, 16 pages, 2012. View at Publisher · View at Google Scholar
  35. J. Yang, Z. Chen, W.-S. Chen, and Y. Chen, “Robust affine invariant descriptors,” Mathematical Problems in Engineering, Article ID 185303, 15 pages, 2011. View at Publisher · View at Google Scholar