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Fast and Easy Blind Deblurring Using an Inverse Filter and PROBE

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Computer Analysis of Images and Patterns (CAIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10425))

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Abstract

PROBE (Progressive Removal of Blur Residual) is a recursive framework for blind deblurring. PROBE is neither a functional minimization approach, nor an open-loop sequential method where blur kernel estimation is followed by non-blind deblurring. PROBE is a feedback scheme, deriving its unique strength from the closed-loop architecture. Thus, with the rudimentary modified inverse filter at its core, PROBE’s performance meets or exceeds the state of the art, both visually and quantitatively. Remarkably, PROBE lends itself to analysis that reveals its convergence properties.

N. Zon, R. Hanocka—equal contributors.

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Notes

  1. 1.

    Using a single-threaded Matlab on a 3.4Ghz CPU.

References

  1. Bar, L., Sochen, N., Kiryati, N.: Semi-blind image restoration via mumford-shah regularization. IEEE Trans. Image Process. 15(2), 483–493 (2006)

    Article  Google Scholar 

  2. Chan, T.F., Wong, C.: Total variation blind deconvolution. IEEE Trans. Image Process. 7, 370–375 (1998)

    Article  Google Scholar 

  3. Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. (SIGGRAPH ASIA 2009) 28(5), Article no. 145 (2009)

    Google Scholar 

  4. Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25, 787–794 (2006). SIGGRAPH 2006 Conference Proceedings, Boston, MA

    Article  Google Scholar 

  5. Hanocka, R., Kiryati, N.: Progressive blind deconvolution. In: Azzopardi, G., Petkov, N. (eds.) CAIP 2015. LNCS, vol. 9257, pp. 313–325. Springer, Cham (2015). doi:10.1007/978-3-319-23117-4_27

    Chapter  Google Scholar 

  6. Komodakis, N., Paragios, N.: MRF-based blind image deconvolution. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7726, pp. 361–374. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37431-9_28

    Chapter  Google Scholar 

  7. Kotera, J., Šroubek, F., Milanfar, P.: Blind deconvolution using alternating maximum a posteriori estimation with heavy-tailed priors. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8048, pp. 59–66. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40246-3_8

    Chapter  Google Scholar 

  8. Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-laplacian priors. In: Neural Information Processing Systems NIPS (2009)

    Google Scholar 

  9. Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: Computer Vision and Pattern Recognition (CVPR) (2011)

    Google Scholar 

  10. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Understanding and evaluating blind deconvolution algorithms. In: Computer Vision and Pattern Recognition (CVPR), pp. 1964–1971 (2009)

    Google Scholar 

  11. Levin, A., Weiss, Y., Durand, F., Freeman, W.T.: Efficient marginal likelihood optimization in blind deconvolution. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2657–2664 (2011)

    Google Scholar 

  12. Michaeli, T., Irani, M.: Nonparametric blind super-resolution. In: IEEE International Conference on Computer Vision (ICCV), pp. 945–952 (2013)

    Google Scholar 

  13. Osher, S., Rudin, L.I.: Feature-oriented image enhancement using shock filters. SIAM J. Numer. Anal. 27(4), 919–940 (1990)

    Article  MATH  Google Scholar 

  14. Perrone, D., Favaro, P.: Total variation blind deconvolution: the devil is in the details. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)

    Google Scholar 

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

    Google Scholar 

  16. Shao, W.Z., Li, H.B., Elad, M.: Bi-\(l_0\)-\(l_2\)-norm regularization for blind motion deblurring. J. Vis. Commun. Image Represent. 33, 42–59 (2015)

    Article  Google Scholar 

  17. Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15549-9_12

    Chapter  Google Scholar 

  18. You, Y.L., Kaveh, M.: A regularization approach to joint blur identification and image restoration. IEEE Trans. Image Process. 5, 416–428 (1996)

    Article  Google Scholar 

  19. Zhou, Y., Komodakis, N.: A MAP-estimation framework for blind deblurring using high-level edge priors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 142–157. Springer, Cham (2014). doi:10.1007/978-3-319-10605-2_10

    Google Scholar 

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Correspondence to Nahum Kiryati .

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Zon, N., Hanocka, R., Kiryati, N. (2017). Fast and Easy Blind Deblurring Using an Inverse Filter and PROBE. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10425. Springer, Cham. https://doi.org/10.1007/978-3-319-64698-5_23

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  • DOI: https://doi.org/10.1007/978-3-319-64698-5_23

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