Skip to main content
SpringerLink
Log in
Book cover

Pacific Rim Conference on Multimedia

PCM 2017: Advances in Multimedia Information Processing – PCM 2017 pp 138–148Cite as

SPOS: Deblur Image by Using Sparsity Prior and Outlier Suppression

  • Conference paper
  • First Online:

  • 2792 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10735))

Abstract

In this paper, we propose an effective and robust method SPOS (Sparsity Prior and Outlier Suppression) for blurry image restoration. First, we combine histogram equalization and prior constrain to obtain the salient structure which contains main texture of image. Second, kernel is estimated by salient structure and sparsity constrain. Final, the blurry image is restored by non-blind deconvolution. The contributions of SPOS lie in two aspects: (1) we combine histogram equalization and sparsity suppression to obtain salient structure; (2) we take kernel outliers into consideration and introduce \(L_0\) norm to suppress kernel’s shape. The experiment results show that SPOS has the better performance compared with the state-of-the-art methods.

This project was supported by Shenzhen Peacock Plan (20130408-183003656), Science and Technology Planning Project of Guangdong Province, China (No. 2014B090910001) and China 863 project under Grant (No. 2015AA015905).

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   155.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18(11), 2419–2434 (2009)

    Article  MathSciNet  Google Scholar 

  2. Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. (TOG) 28, 145 (2009)

    Article  Google Scholar 

  3. Cho, S., Wang, J., Lee, S.: Handling outliers in non-blind image deconvolution. In: 2011 International Conference on Computer Vision, pp. 495–502. IEEE (2011)

    Google Scholar 

  4. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. (TOG) 25, 787–794 (2006)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Goldstein, A., Fattal, R.: Blur-kernel estimation from spectral irregularities. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7576, pp. 622–635. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33715-4_45

    Chapter  Google Scholar 

  7. Jia, J.: Single image motion deblurring using transparency. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  11. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2657–2664. IEEE (2011)

    Google Scholar 

  12. Li, L., Lin, W., Wang, X., Yang, G., Bahrami, K., Kot, A.C.: No-reference image blur assessment based on discrete orthogonal moments. IEEE Trans. Cybern. 46(1), 39–50 (2016)

    Article  Google Scholar 

  13. Liu, Y., Wang, J., Cho, S., Finkelstein, A., Rusinkiewicz, S.: A no-reference metric for evaluating the quality of motion deblurring. ACM Trans. Graph. 32(6), 175–1 (2013)

    Google Scholar 

  14. Michaeli, T., Irani, M.: Blind deblurring using internal patch recurrence. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 783–798. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_51

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  18. Perrone, D., Favaro, P.: Total variation blind deconvolution: the devil is in the details. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2909–2916. IEEE (2014)

    Google Scholar 

  19. Ren, W., Cao, X., Pan, J., Guo, X., Zuo, W., Yang, M.H.: Image deblurring via enhanced low-rank prior. IEEE Trans. Image Process. 25(7), 3426–3437 (2016)

    Article  MathSciNet  Google Scholar 

  20. Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. (TOG) 27, 73 (2008)

    Article  Google Scholar 

  21. Lai, W.-S., Huang, J.-B., Hu, Z., Ahuja, N., Yang, M.H.: A comparative study for single image blind deblurring (2016)

    Google Scholar 

  22. 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). https://doi.org/10.1007/978-3-642-15549-9_12

    Chapter  Google Scholar 

  23. Xu, L., Lu, C., Xu, Y., Jia, J.: Image smoothing via L0 gradient minimization. ACM Trans. Graph. (TOG) 30, 174 (2011)

    Google Scholar 

  24. Xu, L., Zheng, S., Jia, J.: Unnatural L0 sparse representation for natural image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1107–1114 (2013)

    Google Scholar 

  25. Zhang, X., Wang, R., Tian, Y., Wang, W., Gao, W.: Image deblurring using robust sparsity priors. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 138–142. IEEE (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Lishui Road 2199, Nanshan District, Shenzhen, 518055, Guangdong Province, China

    Yiwei Zhang, Ge Li, Wenmin Wang & Ronggang Wang

  2. Academy of Broadcasting Science, SAPPRET, China Fuxingmen Outer Street 2, Xicheng District, Beijing, 100866, China

    Xiaoqiang Guo

Authors
  1. Yiwei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ge Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoqiang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenmin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ronggang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ge Li .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Bing Zeng

  2. University of Chinese Academy of Sciences, Beijing, China

    Qingming Huang

  3. University of Ottawa, Ottawa, Ontario, Canada

    Abdulmotaleb El Saddik

  4. University of Electronic Science and Technology of China, Chengdu, China

    Hongliang Li

  5. Chinese Academy of Sciences, Beijing, China

    Shuqiang Jiang

  6. Harbin Institute of Technology, Harbin, China

    Xiaopeng Fan

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhang, Y., Li, G., Guo, X., Wang, W., Wang, R. (2018). SPOS: Deblur Image by Using Sparsity Prior and Outlier Suppression. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10735. Springer, Cham. https://doi.org/10.1007/978-3-319-77380-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77380-3_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77379-7

  • Online ISBN: 978-3-319-77380-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation