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Probability weighted moments regularization based blind image De-blurring

  • Hussain DawoodEmail author
  • Hassan Dawood
  • Guo Ping
  • Rashid Mehmood
  • Ali Daud
  • Abdullah Alamri
  • Jalal S. Alowibdi
Article
  • 32 Downloads

Abstract

The main objective of blind image de-blurring is to recover a sharp image from a given blurry image. A good estimation of the kernel plays an important role in recovering a sharp image. However, if the local object textures are neglected when the kernel is being estimated, this can lead to over-smoothing or can produce a strong ringing effect. In this paper, a new image regularization term based on the Probability Weighted Moments (PWM) for kernel estimation is proposed named as Probability Weighted Moments Regularization (PWMR). PWMR has the ability to preserve the small local texture structure in an image while minimizing the artifacts. Further, it can preserve the better contrast information between neighboring pixels and their corresponding central pixels in a current sliding window; moreover, it has the ability to resist outliers even in a small sample size. The kernel estimated by PWMR is subsequently used to recover the sharp latent image. An extensive comparison of synthetic and real standard benchmark images indicates the effectiveness of PWMR compared to current state-of-the-art blind image de-blurring methods.

Keywords

Blind image de-blurring Image regularization Kernel estimation Probability weighted moments 

Notes

Acknowledgements

This work is fully supported by the grants from the Joint Re-search Fund in Astronomy (Grant No. U1531242) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS), Prof. Ping Guo is the author to whom all correspondence should be addressed.

References

  1. 1.
    Beck, A.; Teboulle, M.: A fast iterative shrinkage- thresholding algorithm for linear inverse problems. SIAM J Imag Sci, 2, pp. 183–202(2009)Google Scholar
  2. 2.
    Chanand TF, Wong C-K (1998) Total variation blind deconvolution. IEEE Trans Image Process 7:370–375CrossRefGoogle Scholar
  3. 3.
    Cho S.; and Lee, S.: Fast motion deblurring. In ACM Trans Graph (TOG), 28, p. 145(2009)Google Scholar
  4. 4.
    Dawood H, Dawood H, Guo P (2012) Combining the contrast information with WLD for texture classification. IEEE Int Conf Comput Sci Auto Eng (CSAE) 2012:203–207Google Scholar
  5. 5.
    Downton F (1966) Linear estimates with polynomial coefficients. Biometrika 53:129–141MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fergus R, Singh B, Hertzmann A, Roweis ST, Freeman WT (2006) Removing camera shake from a single photograph. ACM Trans Graphics (TOG) 25:787–794CrossRefGoogle Scholar
  7. 7.
    Jiangxin D, Pan J, Su Z, Yang M (2017) Blind image deblurring with outlier handling. Proc IEEE Conf Comput Vision Pattern Recogn IEEE Conf Comput Vision Pattern Recogn (CVPR) 2017:2478–2486Google Scholar
  8. 8.
    Jinsha P, Deqing S, Hanspeter P, Hsuan YM (2016) Blind image deblurring using dark channel prior. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2016:1628–1636Google Scholar
  9. 9.
    Jinsha P, Deqing S, Hanspeter P, Hsuan YM (2017) Deblurring images via Dark Channel prior. IEEE Trans Pattern Anal Mach Intell (PAMI)Google Scholar
  10. 10.
    Krishnan D, Fergus R (2009) Fast image deconvolution using hyper-Laplacian priors. Adv Neural Inform Process Syst (NIPS) 2009:1033–1041Google Scholar
  11. 11.
    Krishnan D, Tay T, Fergus R (2011) Blind deconvolution using a normalized sparsity measure. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2011:233–240Google Scholar
  12. 12.
    Lai WS, Ding JJ, Lin YY, Chuang YY (2015) Blur kernel estimation using normalized color-line priors. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2015:64–72Google Scholar
  13. 13.
    Levin A, Weiss Y (2011) F. Durand, Freeman, W. T.: efficient marginal likelihood optimization in blind deconvolution. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2011:2657–2664Google Scholar
  14. 14.
    Levin A, Fergus R, Durand F, Freeman W (2007) Image and depth from a conventional camera with a coded aperture. ACM Trans Graph (TOG) 26:70CrossRefGoogle Scholar
  15. 15.
    Levin A, Weiss L, Durand F, Freeman WT (2009) Understanding and evaluating blind deconvolution algorithms. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2009:1964–1971Google Scholar
  16. 16.
    Lian J, Zheng Y, Jiao W, Yan F, Zhao B (2018) Deblurring sequential ocular images from multi-spectral imaging (MSI) via mutual information. Med Biol Eng Comput 56(6):1107–1113CrossRefGoogle Scholar
  17. 17.
    Michaeli T, Irani M (2014) Blind deblurring using internal patch recurrence. Eur Conf Comput Vision (ECCV) 2014:783–798Google Scholar
  18. 18.
    Mohammad T, Li Y, Monga V (2018) Blind image Deblurring using row-column sparse representations. IEEE Signal Process Lett (SPL) 25:273–278CrossRefGoogle Scholar
  19. 19.
    Muhammad F, Riaz M (2006) Probability weighted moments approach to quality control charts. Econ Qual Contrl 21:251–260MathSciNetzbMATHGoogle Scholar
  20. 20.
    Muhammad F, Aslam M, Pasha GR (2008) Adaptive estimation of heteroscedastic linear regression model using probability weighted moments. J Mod Appl Stat Methods 7:15CrossRefGoogle Scholar
  21. 21.
    Perrone D, Favaro P (2014) Total variation blind deconvolution: the devil is in the details. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2014:2909–2916Google Scholar
  22. 22.
    Pu H, Fan M, Yang J, Lian J (2018) Quick response barcode deblurring via doubly convolutional neural network. Multimed Tools Appl, pp.1–16Google Scholar
  23. 23.
    Shan Q, Jia J, Agarwala A (2008) High-quality motion deblurring from a single image. ACM Trans Graph (TOG) 27:73Google Scholar
  24. 24.
    Singh D, Kumar V (2017) Modified gain intervention filter based dehazing technique. J Modern Optics (JMO) 64:2165–2178CrossRefGoogle Scholar
  25. 25.
    Singh D, Kumar V (2017) Dehazing of remote sensing images using fourth-order partial differential equations based trilateral filter. IET Comput VisGoogle Scholar
  26. 26.
    Singh D, Kumar V (2018) Defogging of road images using gain coefficient-based trilateral filter. J Electron Imag 27:013004CrossRefGoogle Scholar
  27. 27.
    Whyte O, Sivic J, Zisserman A, Ponce J (2012) Non-uniform deblurring for shaken images. Int J Comput Vision (IJCV) 98:168–186MathSciNetCrossRefGoogle Scholar
  28. 28.
    Wipf D, Zhang H (2013) Analysis of Bayesian blind deconvolution. Int Workshop Energy Minim Meth Comput Vision Pattern Recogn 2013:40–53CrossRefGoogle Scholar
  29. 29.
    Wipf D, Zhang H (2014) Revisiting bayesian blind deconvolution. J Mach Learn Res: 3595–3634Google Scholar
  30. 30.
    Xu L, Jia L (2010) Two-phase kernel estimation for robust motion deblurring. In European Conference on Computer Vision (ECCV) 2010:157–170Google Scholar
  31. 31.
    Xu L, Zheng S, Jia J (2013) Unnatural l0 sparse representation for natural image deblurring. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2013:1107–1114Google Scholar
  32. 32.
    Yue T, Cho S, Wang J, Dai Q (2014) Hybrid image deblurring by fusing edge and power spectrum information. Eur Conf Comput Vision (ECCV) 2014:79–93Google Scholar
  33. 33.
    Zhang H, Wipf D, Zhang Y (2013) Multi-image blind deblurring using a coupled adaptive sparse prior. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2013:1051–1058Google Scholar
  34. 34.
    Zhong DL, Cho S, Metaxas D, Paris S, Wang J (2013) Handling noise in single image deblurring using directional filters. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2013:612–619Google Scholar
  35. 35.
    Zhou Y, Komodakis N (2014) A map-estimation framework for blind deblurring using high-level edge priors. Eur Conf Comput Vision (ECCV) 2014:142–157Google Scholar
  36. 36.
    Zuo W-M, Dongwei R, David Z, Shuhang G, Lei Z (2016) Learning iteration-wise generalized shrinkage–thresholding operators for blind deconvolution. IEEE Trans Image Process (TIP) 25:1751–1764MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer and Network Engineering, College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia
  2. 2.Department of Software EngineeringUniversity of Engineering and TechnologyTaxilaPakistan
  3. 3.School of Systems ScienceBeijing Normal UniversityBeijingPeople’s Republic of China
  4. 4.Department of Software EngineeringUniversity of KotliAzad and Jammu KashmirPakistan
  5. 5.Department of Computer Science & Software EngineeringInternational Islamic UniversityIslamabadPakistan
  6. 6.College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

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