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Gradient Cepstrum Combined with Simplified Extreme Channel Prior for Blind Deconvolution

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Abstract

As a well-known ill-conditional problem in the image processing field, image deblurring has become a hot topic recently. The prior-based blind image deblurring methods have recently shown promising effectiveness. A lot of advanced algorithms such as dark channel prior, bright channel prior, and local maximum gradient prior are time-consuming since nonlinear operators are involved. Presented in this paper is a fast blind image deblurring algorithm which uses the simplified extreme channel prior (SECP) and gradient cepstrum. The inspiration for this work comes from the fact that the simplified bright channel prior (SBCP) of the clear image has fewer non-one elements than the blurred one. We propose a novel SECP based on the proposed SBCP and the simplified dark channel prior (SDCP). By enforcing the \(L_{0}\) norm constraint to the terms involving SECP and incorporating them into the traditional deblurring framework, an effective optimization scheme is explored. Furthermore, gradient cepstrum is used to determine the size of the initial kernel and restrain excessive iterations in each scale. Experimental results illustrate that our algorithm outperforms the state-of-the-art deblurring algorithms in terms of computational efficiency and deblurring effect on both benchmark datasets and real-world blur scenes.

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Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. P. Arbeláez, J. Pont-Tuset, J. Barron, et al. Multiscale combinatorial grouping. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 328–335 (2014)

  2. M.R. Banham, A.K. Katsaggelos, Digital image restoration. IEEE Signal Process. Mag. 42(2), 678–692 (1997)

    Google Scholar 

  3. M. Cannon, Blind deconvolution of spatially invariant image blurs with phase. IEEE Trans. Acoustic Speech Signal Process. 24(1), 58–63 (1976)

    Article  Google Scholar 

  4. P. Campisi, K. Egiazarian, Blind image deconvolution: theory and applications. CRC Press, Boca Raton (2007)

  5. S. Cho, S. Lee, Fast motion deblurring. ACM Trans. Graph. 28(5), 145 (2010)

    Google Scholar 

  6. L. Chen, F. Fang, T. Wang, G. Zhang, Blind image deblurring with local maximum gradient prior. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

  7. A. Chakrabarti, A neural approach to blind motion deblurring. Proc. Eur. Conf. Comput. Vis. 221–235 (2016)

  8. H. Cho, J. Wang, S. Lee, Text image deblurring using text-specific properties. Proc. Eur. Conf. Comput. Vis. 524–537 (2012)

  9. X. Cao, W. Ren, W. Zuo, et al., Scene text deblurring using text-specifific multiscale dictionaries. IEEE Trans. Image Process. p 3426–3437 (2015)

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

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

    Article  Google Scholar 

  12. M. Hirsch, C.J. Schuler, S. Harmeling, B. Schölkopf, Fast removal of non-uniform camera shake. In: 2011 International Conference on Computer Vision, Barcelona, 463–470 (2011)

  13. Z. Hu, S. Cho, J. Wang, M. Yang, Deblurring low-light images with light streaks. In: IEEE Conference on Computer Vision and Pattern Recognition (2014)

  14. N. Joshi, R. Szeliski, D. Kriegman, PSF estimation using sharp edge prediction. In: IEEE Conference on Computer Vision and Pattern Recognition (2008)

  15. D. Krishnan, R. Fergus, Fast Image Deconvolution using Hyper-Laplacian Priors. Adv. Neural Inf. Process. Syst. 1033–1041 (2009)

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

  17. R. Kohler, M. Hirsch, B. Mohler, et al., Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. Proc. Eur. Conf. Comput. Vis. 27–40 (2012)

  18. L.B. Lucy, An iterative technique for the rectification of observed distributions. Astron J. 79(8), 745–754 (1974)

    Article  Google Scholar 

  19. A. Levin, Y. Weiss, et al., Efficient marginal likelihood optimization in blind deconvolution. In: IEEE Conference on Computer Vision and Pattern Recognition, 2657–2664 (2011)

  20. W. Lai, J. Ding, Y. Lin, Y. Chuang, Blur kernel estimation using normalized color-line prior. In: IEEE Conference on Computer Vision and Pattern Recognition, 64–72 (2015)

  21. J. Liu, J. Tan, L. He, X. Ge, D. Hu, Blind Image Deblurring via Local Maximum Difference Prior. IEEE Access 8, 219295–219307 (2020)

    Article  Google Scholar 

  22. L. Li, J. Pan, W. Lai, C. Gao, N. Sang, M. Yang, Blind image deblurring via deep discriminative priors. Int. J. Comput. Vis. 127(8), 1025–1043 (2019)

    Article  Google Scholar 

  23. T. Li, K. Lii, A joint estimation aproach for two-tone image deblurring by blind deconvolution. ACM Trans. Graph, 847–858 (2002)

  24. H. Lee, C. Jung, C. Kim, Blind deblurring of text images using a text-specific hybrid dictionary. IEEE Trans. Image Process, 710–723 (2019)

  25. A. Levin, Y. Weiss, F. Durand, W.T. Freeman, Understanding and evaluating blind deconvolution algorithms. In: IEEE Conference on Computer Vision and Pattern Recognition, 4003–4011 (2009)

  26. W. Lai, J. Huang, Z. Hu, et al., A comparative study for single image blind deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition. 1701–1709 (2019)

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

  28. S. Mingzhu, L. Shuaiqi, PSF Estimation via Gradient Cepstrum Analysis for Image Deblurring in Hybrid Sensor Network. Int. J. Distrib. Sens Netw. (2015)

  29. S. Nah, T.H. Kim, K.M. Lee, Deep multi-scale convolutional neural network for dynamic scene deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition, 257–265 (2017)

  30. J. Pan, D. Sun, H. Pfister, M. Yang, Blind image deblurring using dark channel prior. In: IEEE Conference on Computer Vision and Pattern Recognition, 1628–1636 (2016)

  31. J. Pan, Z. Hu, Z. Su, L0-regularized intensity and gradient prior for deblurring text images and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 342–355 (2016)

    Article  Google Scholar 

  32. J. Pan, Z. Hu, Z. Su, Deblurring face images with exemplars. Eur. Conf. Comput. Vis., 47–62 (2014)

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

    Article  MathSciNet  Google Scholar 

  34. D. Ren, K. Zhang, Q. Wang, Neural blind deconvolution using deep priors. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)

  35. G. Stockham, M. Cannon, Blind deconvolution through digital signal processing. Proc. IEEE 63(4), 678–692 (1975)

    Article  Google Scholar 

  36. L. Sun, S. Cho, J. Wang, J. Hays, Edge-based blur kernel estimation using patch priors. In: Proceedings of the IEEE International Conferenceon Computer Photography, 1–8 (2013)

  37. Q. Shan, J. Jia, A. Agarwala, High-quality motion deblurring from a single image. ACM Trans. Graph. 27(3), 73 (2008)

    Article  Google Scholar 

  38. J. Sun, W. Cao, Z. Xu, J. Ponce, Learning a convolutional neural network for non-uniform motion blur removal. In: IEEE Conference on Computer Vision and Pattern Recognition, 769–777 (2015)

  39. C. Schuler, M. Hirsch, S. Harmeling, Learning to deblur. IEEE Trans. Pattern Anal. Mach. Intell. 38(7), 1439–1451 (2016)

    Article  Google Scholar 

  40. W. Shao, Y. Lin, Y. Liu, L. Wang, H. Li, Gradient-based discriminative modeling for blind image deblurring. Neurocomputing (2020)

  41. X. Tao, H. Gao, X. Shen, J. Wang, J. Jia, Scale-recurrent network for deep image deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition, 8174–8182 (2018)

  42. N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series (MIT press, Cambridge, 1949)

    Book  Google Scholar 

  43. O. Whyte, J. Sivic, A. Zisserman, Deblurring shaken and partially saturated images. Int. Conf. Comput. Vis. 745–752 (2011)

  44. O. Whyte, J. Sivic, A. Zisserman, J. Ponce, Non-uniform deblurring for shaken images. Int. J. Comput. Vis. 98(2), 168–186 (2012)

    Article  MathSciNet  Google Scholar 

  45. F. Wen, R. Ying, Y. Liu, et al., A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring. IEEE Trans. Circuits Syst. Video Technol. 99, 1–1 (2020)

  46. Z. Wang, A. Bovik, H. Sheikh et al., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2014)

    Article  Google Scholar 

  47. Z. Wang, E. Simoncelli, A. Bovik, Multiscale structural similarity for image quality assessment. In: IEEE Asilomar Conference on Signals (2003)

  48. L. Xu, J. Jia, Two-Phase Kernel Estimation for Robust Motion Deblurring. IEEE Int. Conf. Comput. Vis, 157–170 (2010)

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

  50. L. Xu, L. Cewu, Y. Xu, J. Jia, Image smoothing via L0 gradient minimization. ACM Trans. Graph. (2011)

  51. Y. Yan, W. Ren, Y. Guo, R. Wang, X. Cao, Image deblurring via extreme channels prior. In: IEEE Conference on Computer Vision and Pattern Recognition, 4003–4011 (2017)

  52. R. Yan, L. Shao, Blind image blur estimation via deep learning. IEEE Trans. Image Process. 25(4), 1910–1921 (2016)

    MathSciNet  MATH  Google Scholar 

  53. R. Yasarla, F. Perazzi, V. Patel, Deblurring face images using uncertainty guided multi-stream semantic networks. IEEE Trans. Image Process. 29, 6251–6263 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank the reviewers for their helpful comments and suggestions which greatly improve the quality of the paper. This work is supported by the National Natural Science Foundation of China (No.62172135).

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Authors and Affiliations

  1. School of Computer and Information, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Jing Liu & Jieqing Tan

  2. School of Mathematics, Hefei University of Technology, Hefei, 230009, People’s Republic of China

    Lei He

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  1. Jing Liu
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Correspondence to Jieqing Tan.

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Liu, J., Tan, J. & He, L. Gradient Cepstrum Combined with Simplified Extreme Channel Prior for Blind Deconvolution. Circuits Syst Signal Process 41, 1074–1099 (2022). https://doi.org/10.1007/s00034-021-01827-1

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