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A Hybrid Motion Deblurring Strategy Using Patch Based Edge Restoration and Bilateral Filter

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Abstract

Motion blur is a common problem in digital photography. In the dim light, a long exposure time is needed to acquire a satisfactory photograph, and if the camera shakes during exposure, a motion blur is captured. Image deblurring has become a crucial image-processing challenge, because of the increased popularity of handheld cameras. Traditional motion deblurring methods assume that the blur degradation is shift-invariant; therefore, the deblurring problem can be reduced to a deconvolution problem. Edge-specific motion deblurring sharpened the strong edges of the image and then used them to estimate the blur kernel. However, this also enhanced noise and narrow edges, which cause ambiguity and ringing artifacts. We propose a hybrid-based single image motion deblurring algorithm to solve these problems. First, we separated the blurred image into strong edge parts and smooth parts. We applied the improved patch-based sharpening method to enhance the strong edge for kernel estimation, but for the smooth part, we used the bilateral filter to remove the narrow edge and the noise for avoiding the generation of ringing artifacts. Experimental results show that the proposed method is efficient at deblurring for a variety of images and can produce images of a quality comparable to other state-of-the-art techniques.

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References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall, Englewood Cliffs (2002)

    Google Scholar 

  2. Richardson, W.H.: Bayesian-based iterative method of image restoration. J. Optic. Soc. Am. 62(1), 55–59 (1972)

    Article  Google Scholar 

  3. Lee, J.H., Ho, Y.S.: High-quality non-blind image deconvolution with adaptive regularization. J. Vis. Commun. Image Represent. 22(7), 653–663 (2011)

    Article  Google Scholar 

  4. Zhao, J., Feng, H., Xu, Z., Li, Q.: An improved image deconvolution approach using local constraint. Opt. Laser Technol. 44(2), 421–427 (2012)

    Article  Google Scholar 

  5. Rav-Acha, A., Peleg, S.: Two motion-blurred images are better than one. Pattern Recognit. Lett. 26, 311–317 (2005)

    Article  Google Scholar 

  6. Yuan, L., Sun, J., Quan, L., Shum, H.: Image deblurring with blurred/noisy image pairs. ACM Trans. Graph. 26(3), 1 (2007)

    Article  Google Scholar 

  7. Zhuo, S., Guo, D., Sim, T.: Robust flash deblurring. In: IEEE Conference Society on Computer Vision and Pattern Recognition, pp. 2440–2447 (2010)

  8. Zhang, H., Wipf, D., Zhang, Y.: Multi-observation blind deconvolution with an adaptive sparse prior. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1628–1643 (2014)

    Article  Google Scholar 

  9. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25(3), 787–794 (2006)

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding blind deconvolution algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2354 (2011)

    Article  Google Scholar 

  13. Wang, C., Sun, L., Cui, P., Zhang, J., Yang, S.: Analyzing image deblurring through three paradigms. IEEE Trans. Image Process. 21(1), 115–129 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  14. Xu, L., Zheng, S., Jia, J.: Unnatural L0 Sparse Representation for Natural Image Deblurring. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2013)

  15. Wang, C., Yue, Y., Dong, F., Tao, Y., Ma, X., Clapworthy, G., Lin, H., Ye, X.: Nonedge-specific adaptive scheme for highly robust blind motion deblurring of natural images. IEEE Trans. Image Process. 22, 884–897 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Sun, L., Cho, S., Wang, J., Hays, J.: Edge-based Blur Kernel Estimation Using Patch Priors. In: Proceedings of IEEE International Conference on Computational Photography (2013)

  17. Sun, L., Cho, S., Wang, J., Hays, J.: Good image priors for non-blind deconvolution: generic vs specific. In: Proceedings of European Conference on Computer Vision, vol. 8692, pp. 231–246 (2014)

  18. Zuo, W., Meng, D., Zhang, L., Feng, X., Zhang, D.: A generalized iterated shrinkage algorithm for non-convex sparse coding. In: Proceedings of the IEEE international conference on computer vision, pp. 217–224 (2013)

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

    MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  21. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)

    Article  Google Scholar 

  22. Tsai, H.C., Wu, J.L.: An improved adaptive deconvolution algorithm for single image deblurring. Math. Probl. Eng. 2014(3), 1–11 (2014)

    Google Scholar 

  23. Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-Laplacian priors. In: Proceedings of the 23rd Annual Conference on Neural Information Processing Systems(NIPS ’09), pp. 1033–1041 (2009)

  24. Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci. 1, 248–272 (2008)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

This research is supported by the Ministry of Science and Technology, Taiwan, under Grants MOST 104-2221-E-005-090- and MOST105-2221-E-005-068.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Chung Hsing University, 402, Taichung, Taiwan

    Chia-Feng Chang, Jiunn-Lin Wu & Kuan-Jen Chen

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  1. Chia-Feng Chang
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  2. Jiunn-Lin Wu
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  3. Kuan-Jen Chen
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Correspondence to Jiunn-Lin Wu.

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Chang, CF., Wu, JL. & Chen, KJ. A Hybrid Motion Deblurring Strategy Using Patch Based Edge Restoration and Bilateral Filter. J Math Imaging Vis 60, 1081–1094 (2018). https://doi.org/10.1007/s10851-018-0797-x

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  • DOI: https://doi.org/10.1007/s10851-018-0797-x

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