The Visual Computer

, Volume 30, Issue 6–8, pp 661–671 | Cite as

Fast high-quality non-blind deconvolution using sparse adaptive priors

Original Article


We present an efficient approach for high-quality non-blind deconvolution based on the use of sparse adaptive priors. Its regularization term enforces preservation of strong edges while removing noise. We model the image-prior deconvolution problem as a linear system, which is solved in the frequency domain. This clean formulation lends to a simple and efficient implementation. We demonstrate its effectiveness by performing an extensive comparison with existing non-blind deconvolution methods, and by using it to deblur photographs degraded by camera shake. Our experiments show that our solution is faster and its results tend to have higher peak signal-to-noise ratio than the state-of-the-art techniques. Thus, it provides an attractive alternative to perform high-quality non-blind deconvolution of large images, as well as to be used as the final step of blind-deconvolution algorithms.


Non-blind deconvolution Adaptive priors Deblurring Computational photography 



This work was sponsored by CNPq (Grants 482271/2012-4 and 308936/2010-8). We thank the authors of the compared techniques for making their code available, and Shan et al. for providing the photographs and kernels shown in Figs. 9 and10.


  1. 1.
    Banham, M., Katsaggelos, A.: Digital image restoration. Signal Process. Mag. IEEE 14(2), 4–41 (1997)Google Scholar
  2. 2.
    Ben-Ezra, M., Nayar, S.: Motion-based Motion Deblurring. IEEE TPAMI 26(6), 689–698 (2004)CrossRefGoogle Scholar
  3. 3.
    Bonesky, T.: Morozov’s discrepancy principle and tikhonov-type functionals. Inverse Probl. 25(1), 015015 (2009)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Cai, J.-F., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: CVPR 2009. pp 104–111 (2009)Google Scholar
  5. 5.
    Campisi, P., Egiazarian, K.: Blind Image Deconvolution: theory and Applications. CRC Press, Boca Raton (2007)Google Scholar
  6. 6.
    Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 5(28), 145 (2009). (1–145:8)Google Scholar
  7. 7.
    Cho, S., Wang, J., Lee, S.: Handling outliers in non-blind image deconvolution. ICCV 2011. pp 1–8 (2011)Google Scholar
  8. 8.
    Cho, T.S., Zitnick, C.L., Joshi, N., Kang, S.B., Szeliski, R., Freeman, W.T.: Image restoration by matching gradient distributions. IEEE TPAMI 34(4), 683–694 (2012)CrossRefGoogle Scholar
  9. 9.
    Engl, H., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Kluwer, Dordrecht (1996)CrossRefMATHGoogle Scholar
  10. 10.
    Gastal, E.S.L., Oliveira, M.M.: Domain transform for edge-aware image and video processing. ACM TOG 30(4), 69 (2011). (1–12, SIGGRAPH 2011)CrossRefGoogle Scholar
  11. 11.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM TOG 25, 787–794 (2006)CrossRefGoogle Scholar
  12. 12.
    Fortunato, H.E., Oliveira, M.M.: Coding depth through mask structure. Computer graphics forum. In: Proceedings of Eurographics, vol. 31(2), pp. 459–468 (2012)Google Scholar
  13. 13.
    Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization. Image Process. IEEE Trans. 4(7), 932–946 (1995)Google Scholar
  14. 14.
    Joshi, N., Zitnick, C., Szeliski, R., Kriegman, D.: Image deblurring and denoising using color priors. In: CVPR 2009, pp. 1550–1557 (2009)Google Scholar
  15. 15.
    Kodak. Kodak Lossless True Color Image Suite. Accessed Jan 2013
  16. 16.
    Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. Adv. Neural Inf. Process. Syst. 22, 1033–1041 (2009)Google Scholar
  17. 17.
    Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM TOG, vol. 26 (article 70) (2007)Google Scholar
  18. 18.
    Levin, A., Sand, P., Cho, T.S., Durand, F., Freeman, W.T.: Motion-invariant photography. ACM Trans. Graph. 27, 71 (2008). (1–71:9)CrossRefGoogle Scholar
  19. 19.
    Levin, A., Weiss, Y., Durand, F., Freeman, W.: Efficient marginal likelihood optimization in blind deconvolution. In: CVPR 2011, pp. 2657–2664 (2011)Google Scholar
  20. 20.
    Liu, R., Jia, J.: Reducing boundary artifacts in image deconvolution. In: ICIP’08, pp. 505–508 (2008)Google Scholar
  21. 21.
    Lucy, L.B.: An iterative technique for the rectification of observed distributions. Astron. J. 79, 745 (1974)CrossRefGoogle Scholar
  22. 22.
    Shan, Q., Xiong, W., Jia, J.: Rotational motion deblurring of a rigid object from a single image. In: ICCV 2007, pp. 1–8 (2007)Google Scholar
  23. 23.
    Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM TOG 27 (2008)Google Scholar
  24. 24.
    Sibarita, J.B.: Deconvolution microscopy. Adv. Biochem. Eng. Biotechnol. 95, 201–243 (2005)Google Scholar
  25. 25.
    Starck, J.L., Pantin, E., Murtagh, F.: Deconvolution in astronomy: a review. Publications of the Astronomical Society of the Pacific (October), pp. 1051–1069 (2002)Google Scholar
  26. 26.
    Tai, Y.-W., Chen, X., Kim, S., Kim, S.J., Li, F., Yang, J., Yu, J., Matsushita, Y., Brown, M.S.: Nonlinear camera response functions and image deblurring: theoretical analysis and practice. IEEE TPAMI 2013 35, 2498–2512 (2013)Google Scholar
  27. 27.
    Tikhonov, A.N., Arsenin, V.Y.: Solutions of Ill-Posed Problems. Wiley, New York (1977)MATHGoogle Scholar
  28. 28.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. ICCV ’98, pp. 839–846 (1998)Google Scholar
  29. 29.
    Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci. 1(3), 248–272 (2008)Google Scholar
  30. 30.
    Yuan, L., Sun, J., Quan, L., Shum, H.-Y.: Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graph. 27(3), 74 (2008). (1–74:10)Google Scholar
  31. 31.
    Xu, L., Jia, J.: Two-phase Kernel Estimation for Robust Motion Deblurring ECCV’10, pp. 157–170 (2010)Google Scholar
  32. 32.
    Xu, L., Zheng, S., Jia, J.: Unnatural L0 Sparse Representation for Natural Image Deblurring CVPR, pp. 1107–1114 (2013)Google Scholar
  33. 33.
    Zhou, C., Nayar, S.K.: What are good apertures for defocus deblurring? In: ICCP, pp. 1–8 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Uniritter, Laureate International UniversitiesPorto AlegreBrazil
  2. 2.Instituto de Informática, UFRGSPorto AlegreBrazil

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