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Blind Image Deblurring Based on Local Rank

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Abstract

Conventional algorithms for blind image deblurring are often inaccurate at blur kernel estimation, and the recovery effect is far from perfect. To address this, we propose a single-image blind deblurring method based on local rank. For this, we first impose adaptive threshold segmentation on a conventional local rank transform, which is subsequently used to construct a novel model for blind image deblurring. Next, a half-quadratic splitting method is adopted to estimate the blur kernel and an intermediate latent image, in alternating iterations. Finally, the desired latent image is obtained by linear combination of the hyper-Laplacian model and the total-variation-l2 model, where the weights are calculated from the adaptive local ranks. Experimental results using public datasets show that the proposed approach can accurately estimate the blur kernel and effectively suppress ringing effects.

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References

  1. Fergus R, Singh B, Hertzmann A, et al. (2006) Removing camera shake from a single photograph. ACM Trans Graph 25(3):787–794

    MATH  Google Scholar 

  2. Shan Q, Jia J, Agarwala A (2008) High-quality motion deblurring from a single image. ACM Trans Graph 27(3):1

    Google Scholar 

  3. Krishnan D, Tay T, Fergus R (2011) Blind deconvolution using a normalized sparsity measure

  4. Xu L, Zheng S, Jia J (2013) Unnatural l0 sparse representation for natural image deblurring. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR)

  5. Pan J, Su Z (2013) Fast l0-regularized kernel estimation for robust motion deblurring. IEEE Signal Process Lett 20(9):841–844

    Google Scholar 

  6. Shao WZ, Li HB, Elad M (2015) Bi-l0-l2-norm regularization for blind motion deblurring. J Vis Commun Image Represent 33:42–59

    Google Scholar 

  7. Hu Z, Huang JB, Yang MH (2010) Single image deblurring with adaptive dictionary learning. In: Proceedings of IEEE international conference on image processing (ICIP), pp 1169–1172

  8. Zhang H, Yang J, Zhang Y et al (2011) Sparse representation based blind image deblurring. In: Proceedings of IEEE international conference on multimedia and expo (ICME), pp 1–6

  9. Cai JF, Ji H, Liu C et al (2012) Framelet-based blind motion deblurring from a single image. IEEE Trans Image Process 21(2):562–572

    MathSciNet  MATH  Google Scholar 

  10. Ren W, Cao X, Pan J et al (2016) Image deblurring via enhanced low-rank prior. IEEE Trans Image Process 25(7):3426–3437

    MathSciNet  MATH  Google Scholar 

  11. Pan J, Sun D, Pfister H et al (2016) Blind image deblurring using dark channel prior. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR)

  12. Yan Y, Ren W, Guo Y et al (2017) Image deblurring via extreme channels prior. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR)

  13. Chan TF, Wong CK (1998) Total variation blind deconvolution. IEEE Trans Image Process 7(3):370–375

    Google Scholar 

  14. Perrone D, Favaro P (2014) Total variation blind deconvolution: the devil is in the details. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2909–2916

  15. Cho S, Lee S (2009) Fast motion deblurring. ACM Trans Graph 28(5):145

    Google Scholar 

  16. Xu L, Jia J (2010) Two-phase kernel estimation for robust motion deblurring. In: Proceedings of IEEE European conference on computer vision (ECCV), pp 157–170

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

    Google Scholar 

  18. Dong J, Pan J, Su Z et al (2017) Blind image deblurring with outlier handling. In: Proceedings of IEEE international conference on computer vision (ICCP), pp 2478–2486

  19. Hu Z, Cho S, Wang J et al (2014) Deblurring low-light images with light streaks. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 3382–3389

  20. Cho H, Wang J, Lee S (2012) Text image deblurring using text-specific properties. In: Proceedings of European conference on computer vision (ECCV), pp 524–537

  21. Pan J, Hu Z, Su Z et al (2014) Deblurring text images via l0-regularized intensity and gradient prior. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 2901–2908

  22. Pan J, Hu Z, Su Z et al (2014) Deblurring face images with exemplars. In: Proceedings of IEEE European conference on computer vision (ECCV), pp 47–62

  23. Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. In: Proceedings of IEEE European conference on computer vision (ECCV), pp 151–158

  24. Mukherjee J (2011) Local rank transform: properties and applications. Pattern Recogn Lett 32(7):1001–1008

    Google Scholar 

  25. Gong W, Hu L, Li J et al (2015) Combining sparse representation and local rank constraint for single image super resolution. Inf Sci 325:1–19

    MathSciNet  Google Scholar 

  26. Lan R, Zhou Y, Tang YY (2016) Quaternionic local ranking binary pattern: a local descriptor of color images. IEEE Trans Image Process 25(2):566–579

    MathSciNet  MATH  Google Scholar 

  27. Krishnan D, Fergus R (2009) Fast image deconvolution using hyper-Laplacian priors. In: Proceedings of IEEE advances in neural information processing systems (NIPS), pp 1033–1041

  28. Chan SH, Khoshabeh R, Gibson KB, et al. (2011) An augmented Lagrangian method for total variation video restoration. IEEE Trans Image Process 20(11):3097–3111

    MathSciNet  MATH  Google Scholar 

  29. Zhang X, Wang R, Tian Y et al (2015) Image deblurring using robust sparsity priors. In: Proceedings of IEEE international conference on image processing (ICIP)

  30. Köhler R, Hirsch M, Mohler B et al (2012) Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. In: Proceedings of European conference on computer vision (ECCV), pp 27–40

  31. Levin A, Weiss Y, Durand F et al (2009) Understanding and evaluating blind deconvolution algorithms. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 1964–1971

  32. Zhong L, Cho S, Metaxas D et al (2013) Handling noise in single image deblurring using directional filters. In: Proceedings of IEEE computer vision and pattern recognition (CVPR)

  33. Liu Y, Wang J, Cho S et al (2013) A no-reference metric for evaluating the quality of motion deblurring. ACM Trans Graph 32(6):1–12

    Google Scholar 

  34. Zhou Q, Yang W, Gao G et al (2019) Multi-scale deep context convolutional neural networks for semantic segmentation. World Wide Web 22(2):555–570

    Google Scholar 

  35. Zhou Q, Zheng B, Zhu W et al (2016) Multi-scale context for scene labeling via flexible segmentation graph. Pattern Recogn 59:312–324

    Google Scholar 

  36. Zhao W, Lu H, Wang D (2018) Multisensor image fusion and enhancement in spectral total variation domain. IEEE Trans Multimedia 20(4):866–879

    Google Scholar 

  37. Zhao W, Zhao F, Wang D et al (2018) Multisensor image fusion and enhancement in spectral total variation domain. In: The IEEE conference on computer vision and pattern recognition (CVPR), pp 3080–3088

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant no. 61573273. It is also supported by the State Key Laboratory of Rail Transit Engineering Informatization (FSDI) under Grant no.SKLKZ19-01.

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Li Zhu, Long Jin, Jihua Zhu, Zhongyu Li & Zhiqiang Tian

  2. Kyushu Institute of Technology, Fukuoka, Japan

    Huimin Lu

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  1. Li Zhu
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  2. Long Jin
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  3. Jihua Zhu
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  4. Zhongyu Li
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  5. Zhiqiang Tian
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  6. Huimin Lu
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Correspondence to Jihua Zhu.

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Zhu, L., Jin, L., Zhu, J. et al. Blind Image Deblurring Based on Local Rank. Mobile Netw Appl 25, 1446–1456 (2020). https://doi.org/10.1007/s11036-019-01375-8

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