Skip to main content
SpringerLink
Log in

Blind motion image deblurring using an effective blur kernel prior

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Blind image deblurring, i.e., reconstructing a sharp version of a blurred image, is generally an ill-posed problem, as both the blur kernel and the sharp image are unknown. To solve such problem, one must use effective image and blur kernel priors. In this paper, a blind image deblurring method is proposed, which uses an effective image prior based on both the first and second order gradients of the image. This prior causes to properly reconstruct salient edges which provide reliable edge information for kernel estimation in the intermediate latent image. This prior along with a hyper-Laplacian kernel prior can be used to solve the optimization problem in the form of maximum-a posteriori-problem, and hence obtaine the blur kernel with a high accuracy. The efficiency of the proposed method is demonstrated by performing several quantitative and qualitative comparisons with the state-of-the-art methods, on both a benchmark image dataset and real-world motion blurred images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Cho S, Lee S (2009) Fast motion deblurring. In: ACM Transactions on Graphics (TOG), vol. 28, p. 145, ACM

  2. Cho TS, Paris S, Horn BK, Freeman WT (2011) Blur kernel estimation using the radon transform. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, p 241–248, IEEE

  3. Fergus R, Singh B, Hertzmann A, Roweis ST, Freeman WT (2006) Removing camera shake from a single photograph. ACM Trans Graph 25(3):787–794

    Article  MATH  Google Scholar 

  4. Fortunato HE, Oliveira MM (2014) Fast high-quality non-blind deconvolution using sparse adaptive priors. Vis Comput 30(6–8):661–671

    Article  Google Scholar 

  5. Geman D, Reynolds G (1992) Constrained restoration and the recovery of discontinuities. IEEE Trans Pattern Anal Mach Intell (3):367–383

  6. Geman D, Yang C (1995) Nonlinear image recovery with half-quadratic regularization. IEEE Trans Image Process 4(7):932–946

    Article  Google Scholar 

  7. Javaran TA, Hassanpour H, Abolghasemi V (2016) A noise-immune no-reference metric for estimating blurriness value of an image. Signal Process Image Commun 47:218–228

    Article  Google Scholar 

  8. Javaran TA, Hassanpour H, Abolghasemi V (2017) Local motion deblurring using an effective image prior based on both the first-and second-order gradients. Mach Vis Appl 28(3–4):431–444

    Google Scholar 

  9. Joshi N, Szeliski R, Kriegman DJ (2008) Psf estimation using sharp edge prediction. In: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, p 1–8, IEEE

  10. Joshi N, Zitnick CL, Szeliski R, Kriegman DJ (2009) Image deblurring and denoising using color priors. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, p 1550–1557, IEEE

  11. Krishnan D, Fergus R (2009) Fast image deconvolution using hyper-laplacian priors. In: Advances in Neural Information Processing Systems, p 1033–1041

  12. Krishnan D, Tay T, Fergus R (2011) Blind deconvolution using a normalized sparsity measure. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, p 233–240, IEEE

  13. Levin A, Fergus R, Durand F, Freeman WT (2007) Deconvolution using natural image priors. Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory

  14. Levin A, Weiss Y, Durand F, Freeman WT (2009) Understanding and evaluating blind deconvolution algorithms. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, p 1964–1971, IEEE

  15. Levin A, Weiss Y, Durand F, Freeman WT (2011) Understanding blind deconvolution algorithms. IEEE Trans Pattern Anal Mach Intell 33(12):2354–2367

    Article  Google Scholar 

  16. Liu Q, Sun L, Shao Z (2016) Compound l 0 regularization method for image blind motion deblurring. J Electron Imaging 25(5):053013

    Article  Google Scholar 

  17. Osher S, Rudin LI (1990) Feature-oriented image enhancement using shock filters. SIAM J Numer Anal 27(4):919–940

    Article  MATH  Google Scholar 

  18. Pan J, Su Z (2013) Fast-regularized kernel estimation for robust motion deblurring. Signal Process-Image Commu 20(9):841–844

    Google Scholar 

  19. Pan J, Liu R, Su Z, Gu X (2013) Kernel estimation from salient structure for robust motion deblurring. Signal Process Image Commun 28(9):1156–1170

    Article  Google Scholar 

  20. Pan J, Liu R, Su Z, Liu G (2014) Motion blur kernel estimation via salient edges and low rank prior. In: Multimedia and Expo (ICME), 2014 IEEE International Conference on, p 1–6, IEEE

  21. Roth S, Black MJ (2005) Fields of experts: a framework for learning image priors. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2, p 860–867, IEEE

  22. Shan Q, Jia J, Agarwala A (2008) High-quality motion deblurring from a single image. In: ACM Transactions on Graphics (TOG), vol. 27, p 73, ACM

  23. Shao W-Z, Li H-B, Elad M (2015) Bi-l 0-l 2-norm regularization for blind motion deblurring. J Vis Commun Image Represent 33:42–59

    Article  Google Scholar 

  24. Sun L, Cho S, Wang J, Hays J (2013) Edge-based blur kernel estimation using patch priors. In: Computational Photography (ICCP), 2013 IEEE International Conference on, p 1–8, IEEE

  25. Wang Y, Yang J, Yin W, Zhang Y (2008) A new alternating minimization algorithm for total variation image reconstruction. IEEE T Image Proces 1(3):248–272

    MathSciNet  MATH  Google Scholar 

  26. Weiss Y, Freeman WT (2007) What makes a good model of natural images? In: Computer Vision and Pattern Recognition, 2007. CVPR’07. IEEE Conference on, p 1–8, IEEE

  27. Xiong N, Liu RW, Liang M, Wu D, Liu Z, Wu H (2017) Effective alternating direction optimization methods for sparsity-constrained blind image deblurring. Sensors 17(1):174

    Article  Google Scholar 

  28. Xu L, Jia J (2010) Two-phase kernel estimation for robust motion deblurring. Springer, Berlin, pp 157–170

    Google Scholar 

  29. Xu L, Zheng S, Jia J (2013) Unnatural l0 sparse representation for natural image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p 1107–1114

Download references

Author information

Authors and Affiliations

  1. Image Processing and Data Mining (IPDM) Research Lab, Faculty of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran

    Taiebeh Askari Javaran & Hamid Hassanpour

  2. Computer Science Department, Faculty of Mathematics and Computer, Higher Education Complex of Bam, Bam, Iran

    Taiebeh Askari Javaran

  3. Faculty of Electrical Engineering and Robotics, Shahrood University of Technology, Shahrood, Iran

    Vahid Abolghasemi

Authors
  1. Taiebeh Askari Javaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Hassanpour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vahid Abolghasemi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Taiebeh Askari Javaran.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This prior along with a hyper-Laplacian blur kernel prior can be used to solve the optimization problem in the form of maximum-a posteriori-problem, and hence to obtain the blur kernel with a high accuracy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javaran, T.A., Hassanpour, H. & Abolghasemi, V. Blind motion image deblurring using an effective blur kernel prior. Multimed Tools Appl 78, 22555–22574 (2019). https://doi.org/10.1007/s11042-019-7402-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7402-1

Keywords

Search

Navigation