Blind Image Deblurring with Modified Richardson-Lucy Deconvolution for Ringing Artifact Suppression

  • Hao-Liang Yang
  • Yen-Hao Chiao
  • Po-Hao Huang
  • Shang-Hong Lai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)


In this paper, we develop a unified image deblurring framework that consists of both blur kernel estimation and non-blind image deconvolution. For blind kernel estimation, we propose a patch selection procedure and integrate it with a coarse-to-fine kernel estimation algorithm to develop a robust blur kernel estimation algorithm. For the non-blind image deconvolution, we modify the traditional Richardson-Lucy (RL) image restoration algorithm to suppress the notorious ringing artifact in the regions around strong edges. Experimental results on some real blurred images are shown to demonstrate the improved efficiency and image restoration by using the proposed algorithm.


Image Restoration Kernel Estimation Bilateral Filter Strong Edge Ringing Artifact 
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.
    Richardson, W.H.: Bayesian-based iterative method of image restoration. Journal of the Optical Society of America 62, 55–59 (1972)CrossRefGoogle Scholar
  2. 2.
    Lucy, L.B.: An iterative technique for the rectification of observed distributions. Astronomical Journal 79, 745–765 (1974)CrossRefGoogle Scholar
  3. 3.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: ICCV, pp. 839–847 (1998)Google Scholar
  4. 4.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graphics 25, 787–794 (2006)CrossRefGoogle Scholar
  5. 5.
    Fattal, R., Lischinski, D., Werman, M.: Gradient domain high dynamic range compression. ACM Trans. Graphics, 249–256 (2002)Google Scholar
  6. 6.
    Levin, A.: Blind motion deblurring using image statistics. In: NIPS, pp. 841–848 (2006)Google Scholar
  7. 7.
    Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graphics 26, 70 (2007)CrossRefGoogle Scholar
  8. 8.
    Yuan, L., Sun, J., Quan, L., Shum, H.-Y.: Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graphics 27, 1–10 (2008)CrossRefGoogle Scholar
  9. 9.
    Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graphics 27, 73–83 (2008)Google Scholar
  10. 10.
    Yuan, L., Sun, J., Quan, L., Shum, H.-Y.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graphics 26 (2007)Google Scholar
  11. 11.
    Cho, S., Lee, S.: Fast motion delurring. ACM Trans. Graphics (SIGGRAPH ASIA) (2009)Google Scholar
  12. 12.
    Xu, L., Jia, J.: Two-Phase Kernel Estimation For Robust Motion Deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Harris, C., Stephens, M.: A Combined corner and edge detector. In: Alvey Vision Conference (1988)Google Scholar
  14. 14.
    Joshi, N., Szeliski, R., Kriegman, D.: PSF estimation using sharp edge prediction. In: CVPR (2008)Google Scholar
  15. 15.
    Levin, A., Sand, P., Cho, T.S., Durand, F., Freeman, W.T.: Motion-invariant photography. In: SIGGRAPH (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hao-Liang Yang
    • 1
  • Yen-Hao Chiao
    • 1
  • Po-Hao Huang
    • 1
  • Shang-Hong Lai
    • 1
  1. 1.Dept. of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan

Personalised recommendations