Probability weighted moments regularization based blind image De-blurring

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.

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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)

    MathSciNet  Article  Google Scholar 

  2. 2.

    Chanand TF, Wong C-K (1998) Total variation blind deconvolution. IEEE Trans Image Process 7:370–375

    Article  Google Scholar 

  3. 3.

    Cho S.; and Lee, S.: Fast motion deblurring. In ACM Trans Graph (TOG), 28, p. 145(2009)

    Article  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) 2012:203–207

    Google Scholar 

  5. 5.

    Downton F (1966) Linear estimates with polynomial coefficients. Biometrika 53:129–141

    MathSciNet  Article  Google 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) 25:787–794

    Article  Google 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) 2017:2478–2486

    Google 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) 2016:1628–1636

    Google 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)

  10. 10.

    Krishnan D, Fergus R (2009) Fast image deconvolution using hyper-Laplacian priors. Adv Neural Inform Process Syst (NIPS) 2009:1033–1041

    Google Scholar 

  11. 11.

    Krishnan D, Tay T, Fergus R (2011) Blind deconvolution using a normalized sparsity measure. IEEE Conf Comput Vision Pattern Recogn (CVPR) 2011:233–240

    Google 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) 2015:64–72

    Google 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) 2011:2657–2664

    Google 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) 26:70

    Article  Google 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) 2009:1964–1971

    Google 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):1107–1113

    Article  Google Scholar 

  17. 17.

    Michaeli T, Irani M (2014) Blind deblurring using internal patch recurrence. Eur Conf Comput Vision (ECCV) 2014:783–798

    Google Scholar 

  18. 18.

    Mohammad T, Li Y, Monga V (2018) Blind image Deblurring using row-column sparse representations. IEEE Signal Process Lett (SPL) 25:273–278

    Article  Google Scholar 

  19. 19.

    Muhammad F, Riaz M (2006) Probability weighted moments approach to quality control charts. Econ Qual Contrl 21:251–260

    MathSciNet  MATH  Google 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 7:15

    Article  Google 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) 2014:2909–2916

    Google 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.1–16

  23. 23.

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

    Google Scholar 

  24. 24.

    Singh D, Kumar V (2017) Modified gain intervention filter based dehazing technique. J Modern Optics (JMO) 64:2165–2178

    Article  Google Scholar 

  25. 25.

    Singh D, Kumar V (2017) Dehazing of remote sensing images using fourth-order partial differential equations based trilateral filter. IET Comput Vis

  26. 26.

    Singh D, Kumar V (2018) Defogging of road images using gain coefficient-based trilateral filter. J Electron Imag 27:013004

    Article  Google Scholar 

  27. 27.

    Whyte O, Sivic J, Zisserman A, Ponce J (2012) Non-uniform deblurring for shaken images. Int J Comput Vision (IJCV) 98:168–186

    MathSciNet  Article  Google Scholar 

  28. 28.

    Wipf D, Zhang H (2013) Analysis of Bayesian blind deconvolution. Int Workshop Energy Minim Meth Comput Vision Pattern Recogn 2013:40–53

    Article  Google Scholar 

  29. 29.

    Wipf D, Zhang H (2014) Revisiting bayesian blind deconvolution. J Mach Learn Res: 3595–3634

  30. 30.

    Xu L, Jia L (2010) Two-phase kernel estimation for robust motion deblurring. In European Conference on Computer Vision (ECCV) 2010:157–170

    Google 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) 2013:1107–1114

    Google 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) 2014:79–93

    Google 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) 2013:1051–1058

    Google 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) 2013:612–619

    Google 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) 2014:142–157

    Google 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) 25:1751–1764

    MathSciNet  MATH  Google Scholar 

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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.

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Correspondence to Hussain Dawood.

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Dawood, H., Dawood, H., Ping, G. et al. Probability weighted moments regularization based blind image De-blurring. Multimed Tools Appl 79, 4483–4498 (2020). https://doi.org/10.1007/s11042-019-7520-9

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Keywords

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