Two-Phase Kernel Estimation for Robust Motion Deblurring

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)


We discuss a few new motion deblurring problems that are significant to kernel estimation and non-blind deconvolution. We found that strong edges do not always profit kernel estimation, but instead under certain circumstance degrade it. This finding leads to a new metric to measure the usefulness of image edges in motion deblurring and a gradient selection process to mitigate their possible adverse effect. We also propose an efficient and high-quality kernel estimation method based on using the spatial prior and the iterative support detection (ISD) kernel refinement, which avoids hard threshold of the kernel elements to enforce sparsity. We employ the TV-ℓ1 deconvolution model, solved with a new variable substitution scheme to robustly suppress noise.


Latent Image Kernel Estimation Impulse Noise Motion Blur Blind Deconvolution 
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.


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Jia, J.: Single image motion deblurring using transparency. In: CVPR (2007)Google Scholar
  3. 3.
    Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27 (2008)Google Scholar
  4. 4.
    Joshi, N., Szeliski, R., Kriegman, D.J.: Psf estimation using sharp edge prediction. In: CVPR (2008)Google Scholar
  5. 5.
    Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28 (2009)Google Scholar
  6. 6.
    Cai, J.F., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: CVPR, pp. 104–111 (2009)Google Scholar
  7. 7.
    Joshi, N.: Enhancing photographs using content-specific image priors. PhD thesis, University of California, San Diego (2008)Google Scholar
  8. 8.
    You, Y., Kaveh, M.: Blind image restoration by anisotropic regularization. IEEE Transactions on Image Processing 8, 396–407 (1999)CrossRefGoogle Scholar
  9. 9.
    Chan, T., Wong, C.: Total variation blind deconvolution. IEEE Transactions on Image Processing 7, 370–375 (1998)CrossRefGoogle Scholar
  10. 10.
    Osher, S., Rudin, L.: Feature-oriented image enhancement using shock filters. SIAM Journal on Numerical Analysis 27, 919–940 (1990)zbMATHCrossRefGoogle Scholar
  11. 11.
    Alvarez, L., Mazorra, L.: Signal and image restoration using shock filters and anisotropic diffusion. SIAM J. Numer. Anal. 31 (1994)Google Scholar
  12. 12.
    Gilboa, G., Sochen, N.A., Zeevi, Y.Y.: Regularized shock filters and complex diffusion. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 399–413. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: CVPR (2009)Google Scholar
  14. 14.
    Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graph. 27 (2008)Google Scholar
  15. 15.
    Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26, 70 (2007)CrossRefGoogle Scholar
  16. 16.
    Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal on Imaging Sciences 1, 248–272 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. In: NIPS (2009)Google Scholar
  18. 18.
    Yang, J., Zhang, Y., Yin, W.: An efficient TVL1 algorithm for deblurring multichannel images corrupted by impulsive noise. SIAM J. Sci. Comput. 31, 2842–2865 (2009)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Bar, L., Sochen, N., Kiryati, N.: Image deblurring in the presence of salt-and-pepper noise. In: International Conference on Scale Space and PDE methods in Computer Vision, pp. 107–118 (2005)Google Scholar
  20. 20.
    Joshi, N., Zitnick, C.L., Szeliski, R., Kriegman, D.J.: Image deblurring and denoising using color priors. In: CVPR, pp. 1550–1557 (2009)Google Scholar
  21. 21.
    Yuan, L., Sun, J., Quan, L., Shum, H.Y.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. 26, 1 (2007)zbMATHCrossRefGoogle Scholar
  22. 22.
    Wang, Y., Yin, W.: Compressed Sensing via Iterative Support Detection. CAAM Technical Report TR09-30 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong Kong 

Personalised recommendations