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Morphological Component Image Restoration by Employing Bregmanized Sparse Regularization and Anisotropic Total Variation

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Abstract

Image deblurring is a fundamental problem in imaging field which often needs to recover the important structure of images. This paper addresses the image deblurring problem by considering an image as a combination of its cartoon (the piecewise smooth part of the image) and texture (the oscillation part of the image) components. To recover both of these parts, we propose the use of coupled analysis-based sparse representations to regularize the cartoon structure and the texture part of the image. We apply anisotropic total variation with a quadratic term to enhance the edges existing in the cartoon part. Furthermore, we develop a multivariable Bregman optimization method to solve the proposed image restoration model by combining the alternating minimization method and the split Bregman iteration. The experiments show that the proposed algorithm not only performs well for image decomposition, but also outperforms the previously established methods in terms of the visual residual error, the structure similarity index and the peak signal-to-noise ratio for image deblurring.

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References

  1. N. Ahmed, T. Natarajan, K. Rao, Discrete cosine transform. IEEE Trans. Image Comput. C-23 100(1), 90–93 (1974)

    Article  MathSciNet  Google Scholar 

  2. A. Buades, J. Lisani, Directional filters for color cartoon + texture image and video decomposition. J. Math. Imaging Vis. 55, 125–135 (2016)

    Article  MathSciNet  Google Scholar 

  3. A. Buades, B. Coll, J. Morel, Review of image denoising algorithms with a new one. Multiscale Modeling Simul. 4(2), 490–530 (2005)

    Article  MathSciNet  Google Scholar 

  4. A. Buades, T. Le, J.-M. Morel, L. Vese, Fast cartoon + texture image filters. IEEE Trans. Image Process. 19(8), 1978–1985 (2010)

    Article  MathSciNet  Google Scholar 

  5. J. Cai, Z. Shen, Framelet based deconvolution. J. Comput. Math. 282, 89–308 (2010)

    MathSciNet  MATH  Google Scholar 

  6. J. Cai, S. Osher, Z. Shen, Split Bregman method and Frame based image restoration. SIAM Multiscale Modeling Simul. 8(2), 337–369 (2009)

    Article  MathSciNet  Google Scholar 

  7. A. Chai, Z. Shen, Deconvlution: a wavelet frame approach. Numerische athematik 106, 529–587 (2007)

    Article  Google Scholar 

  8. A. Chambolle, An algorithm for total variation minimization and applications. J. Math. Imag. Vision 20(1–2), 89–97 (2004)

    MathSciNet  MATH  Google Scholar 

  9. T. Chan, S. Esedoglu, Aspects of total variation regularized L1 function approximation. SIAM J. Appl. Math. 65(5), 1817–1837 (2005)

    Article  MathSciNet  Google Scholar 

  10. T. Chan, C. Wong, Convergence of the alternating minimization algorithm for blind deconvolution. Linear Algebra Appl. 316, 259–285 (2000)

    Article  MathSciNet  Google Scholar 

  11. T. Chan, S. Esedoglu, F. Park, A. Yip, et al., Recent developments in total variation image restoration, in Mathematical Models of Computer Vision, ed. by Nagy, J (Springer: New York, 2005)

    Google Scholar 

  12. H. Chen, K. Yan, J. Zhang, Z. Li, Simultaneous cartoon plus texture image decomposition by using variational image decomposition. Proc. SPIE 92732Q, 1–7 (2014)

    Google Scholar 

  13. H. Chen, C. Wang, Y. Song, Z. Li, Split Bregmanized anisotropic total variation model for image deblurring. J. Vis. Commun. Image Represent. R31, 282–293 (2015)

    Article  Google Scholar 

  14. H. Chen, Y. Song, Z. Zhang, Z. Li, Motion blind deblurring method by using anisotropic total variation. J. Optoelectron·Laser 26(6), 1206–1214 (2015)

    Google Scholar 

  15. H. Chen, Q. Wang, C. Wang, Z. Li, Image decomposition based blind image deconvolution model by employing sparse representation. IET Image Proc. 10(11), 908–925 (2016)

    Article  Google Scholar 

  16. H. Chen, X. Qu, Q. Wang, Z. Li, A cartoon-texture decomposition based image deblurring model by using framelet based sparse representation. Proc. SPIE 1002012, 1–13 (2016)

    Google Scholar 

  17. R. Choksi, Y. Gennipy, A. Obermanz, Anisotropic total variation regularized L1 approximation and denoising/deblurring of 2D bar codes. Inverse Problem Imaging 5(3), 591–617 (2011)

    Article  Google Scholar 

  18. K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian, Image restoration by sparse 3D transform-domain collaborative filtering. Proc. SPIE 6812, 681207 (2008)

    Article  Google Scholar 

  19. I. Daubechies, B. Han, A. Ron, Z. Shen, Framelets: MRA-based constructions of wavelet frames. Appl. Comput. Harmon. Anal. 14, 1–46 (2003)

    Article  MathSciNet  Google Scholar 

  20. S. Esedoḡlu, S. Osher, Decomposition of images by the anisotropic Rudin-Osher-Fatemi model. Commun. Pure Appl. Math. 57(12), 1609–1626 (2004). https://doi.org/10.1002/cpa.20045

    Article  MathSciNet  MATH  Google Scholar 

  21. G. Gilboa, S. Osher, Nonlocal operators with applications to image processing. Multiscale Modeling Simul. 7(3), 1005–1028 (2008)

    Article  MathSciNet  Google Scholar 

  22. T. Goldstein, S. Osher, The split Bregman method for L1 regularized problems. SIAM J. IMAGING SCIENCES 2(2), 323–343 (2009)

    Article  MathSciNet  Google Scholar 

  23. Y. Meyer, Oscillating patterns in image processing and nonlinear evolution equations, University Lecture series, 22 (American Mathematical Society, Providence, 2002)

    Google Scholar 

  24. M. Ng, X. Yuan, W. Zhang, Coupled variational image decomposition model for blurred cartoon and texture images with missing pixels. IEEE Trans. Image Process. 22(6), 2233–2246 (2013)

    Article  MathSciNet  Google Scholar 

  25. J.P. Oliveria, J.M. Bioucas-Dias, M. Figueiredo, Adaptive total variation image deblurring: a majorization-minimization approach. Sig. Process. 89, 1683–1693 (2009)

    Article  Google Scholar 

  26. R. Puetter, T. Gosnell, A. Yahil, Digital image reconstruction: deblurring and Denoising. Astronomy and astrophysics review 43, 139–194 (2005)

    Article  Google Scholar 

  27. A. Ron, Z. Shen, Affine systems in L2(Rd): the analysis of the analysis operator. J. Funct. Anal. 148, 408–447 (1997)

    Article  MathSciNet  Google Scholar 

  28. L.I. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms. Physics D: Nonlinear Phenomena 60, 259–268 (1992)

    Article  MathSciNet  Google Scholar 

  29. J.-L. Starck, M. Elad, D. Donoho, Image decomposition via the combination of sparse representation and a variational approach. IEEE Trans. Image Process. 14(10), 1570–1582 (2005)

    Article  MathSciNet  Google Scholar 

  30. S. Tang, W. Gong, W. Li, W. Wang, Non-blind image deblurring method by local and nonlocal total variation models. Sig. Process. 94, 339–349 (2014)

    Article  Google Scholar 

  31. Z. Wang, A. Bovik, H. Sheikh, E. Simoncelli, Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  32. Y. Wang, J. Yang, W. Yin, Y. Zhang et al., A new alternating minimization algorithm for total variation image reconstruction. SIAM Journal of Imaging Sciences 1(3), 248–272 (2008)

    Article  MathSciNet  Google Scholar 

  33. S. Wang, Z. Liu, W. Dong, L. Jiao, Q. Tang, Total variation based image deblurring with nonlocal self-similarity constraint. Electron. Lett. 47(16), 916–918 (2011)

    Article  Google Scholar 

  34. C. Wu, X.C. Tai, Augmented Lagrangian method, dual methods, and split Bregman iteration for ROF, Vectorial TV, and high order models. Siam Journal on Imaging Sciences 3(3), 300–339 (2012)

    Article  MathSciNet  Google Scholar 

  35. X. Zhang, M. Burger, X. Bresson, S. Osher, Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM Journal of Imaging Sciences 3(3), 253–276 (2010)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Fund of China (No. 61371167). We thank Prof. Cai, Prof. Zhang, Prof. Vese et al. for providing the corresponding software online. We also thank Irina Entin, M.Eng., from Liwen Bianji, Edanz Editing China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript.

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Correspondence to Huasong Chen.

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Chen, H., Fan, Y., Wang, Q. et al. Morphological Component Image Restoration by Employing Bregmanized Sparse Regularization and Anisotropic Total Variation. Circuits Syst Signal Process 39, 2507–2532 (2020). https://doi.org/10.1007/s00034-019-01268-x

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