Skip to main content
Springer Nature Link
Log in

PSF-Constraints Based Iterative Blind Deconvolution Method for Image Deblurring

  • Conference paper
Advances in Multimedia Modeling (MMM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5916))

Included in the following conference series:

  • 2270 Accesses

Abstract

In recent years, Image Deblurring techniques have played an essential role in the field of Image Processing. In image deblurring, there are several kinds of blurred image such as motion blur, defocused blur and gaussian blur. Many methods to address this problem have been proposed by researchers in previous research, among which the iterative blind deconvolution (IBD) method is the most popular method to solve this problem. However, the convergence of this method is not ensured, and there is no effective method to choose a proper initial estimate image and PSF(point spread function). In this paper, we improve the iterative blind deconvolution method by adding several constraints, which could be the type or parameters range of the PSF, on the PSF in each iteration. Experiment results validate that, with the help of these newly added constraints, our method are more likely to converge and has better deblurring performance than the IBD.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Katsaggelos, A.K.: Digital Image Restoration. Springer Series in Information Science. Springer, Berlin (1991)

    Google Scholar 

  2. Andrews, H.C., Hunt, B.R.: Digital Image Restoration. Prentice-Hall Signal Processing Series. Prentice-Hall, Englewood Cliffs (1977)

    Google Scholar 

  3. Kundur, D., Hatzinakos, D.: Blind image deconvolution revisited. IEEE Signal Processing Mag. 13, 43–64 (1996)

    Article  Google Scholar 

  4. Fu, S.Y., Zhang, Y.C., Cheng, L., Liang, Z.Z., Hou, Z.G., Tan, M.: Motion Based Image Deblur Using Recurrent Neural Network for Power Transmission Line Inspection Robot. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 3854–3859 (2006)

    Google Scholar 

  5. Ayers, G.R., Dainty, J.C.: Iterative blind deconvolution method and its applications. Optics Letters 13, 547–549 (1988)

    Article  Google Scholar 

  6. Davey, B.L.K., Lane, R.G., Bates, R.H.T.: Blind deconvolution of a noisy complex-valued image. Optics Communications 69, 353–356 (1989)

    Article  Google Scholar 

  7. Miura, N., Baba, N.: Extended-object reconstruction with sequential use of the iterative blind deconvolution method. Optics Communications 89, 375–379 (1992)

    Article  Google Scholar 

  8. Tsumuraya, F., Miura, N., Baba, N.: Iterative blind deconvolution method using Lucys algorithm. Astron Astrophys. 282, 699–708 (1994)

    Google Scholar 

  9. Miura, N., Ohsawa, K., Baba, N.: Single -frame blind deconvolution by means of frame segmentation. Optics Letters 19, 695–697 (1994)

    Article  Google Scholar 

  10. Thibaut, E., Conan, J.: Strict a priori constraints for maximum-likelihood blind deconvolution. Journal of the Optical Society of America A (1995)

    Google Scholar 

  11. Biemond, J., Lagendijk, R.L., Mersereau, R.M.: Iterative methods for image deblurring. Proc IEEE 78, 856–883 (1990)

    Article  Google Scholar 

  12. Money, J.H., Kang, S.H.: Total variation minimizing blind deconvolution with shock filter reference. Image and Vision Comput. 26, 302–314 (2008)

    Article  Google Scholar 

  13. Prasath, V.B.S., Singh, A.: Ringing artifact reduction in blind image deblurring and denoising problems by regularization methods. In: Seventh International Conference on Advances in Pattern Recognition (2009)

    Google Scholar 

  14. Carasso, A.: Direct blind deconvolution. SIAM Journal on Applied Mathematics 61, 1980–2007 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  15. Liang, X.B., Wang, J.: A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints. IEEE Transactions on Neural Networks 11, 1251–1262 (2000)

    Article  Google Scholar 

  16. Rekleitis, I.M.: Optical Flow Recognition From The Power Spectrum of A Single Blurred Image. In: Image Processing, 1996. Proceedings, International Conference, vol. 3, pp. 791–794 (1996)

    Google Scholar 

  17. Yang, Y., Galatsanos, N.P., Stark, H.: Projection-based blind deconvolution. Journal of the Optical Society of America 11, 2401–2409 (1994)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China

    Xuan Mo

  2. Dept. of Computer Science and Technology, Nanjing University, Nanjing, China

    Jun Jiao

  3. School of Software and Electronics, Peking University, Beijing, China

    Chen Shen

Authors
  1. Xuan Mo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Oldenburg, Germany

    Susanne Boll

  2. University of Texas at San Antonio,, TX, San Antonio, USA

    Qi Tian

  3. Microsoft Research Asia, Beijing, P.R. China

    Lei Zhang

  4. Southwest University, Beibei, Chongqing, China

    Zili Zhang

  5. School of Engineering and Information Technology, Deakin University, 221 Burwood Highway, Vic, 3125, Australia

    Yi-Ping Phoebe Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mo, X., Jiao, J., Shen, C. (2010). PSF-Constraints Based Iterative Blind Deconvolution Method for Image Deblurring. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11301-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11300-0

  • Online ISBN: 978-3-642-11301-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics