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Combining Compact Finite Difference Schemes with Filters for Image Restoration

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In this study, the Rudin, Osher, and Fatemi (ROF) model is considered to restore images. To this end, the compact finite difference (CFD) method is introduced to approximate the spatial derivatives in the ROF model. Moreover, two filters are presented to improve the performance of the first- and second-order derivative approximation. The third-order total variation diminishing Runge-Kutta (TVD-RK3) method is applied to solve the obtained system along the time axis. Two examples are given to show the efficiency and accuracy of the method.

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References

  1. P. C. Hansen, J. G. Nagy and D. P. O’Leary, Deblurring Images: Matrices Spectra and Filtering, SIAM, Philadelphia (2006).

    Book  Google Scholar 

  2. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition, Prentice Hall, New Jersey (2002).

  3. A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliffs, NJ (1989).

    MATH  Google Scholar 

  4. T. F. Chan and J. H. Shen, Image Processing and Analysis: Variational, PDE, Wavelet and Stochastic Methods, SIAM, Philadelphia (2005).

    Book  Google Scholar 

  5. C. V. Loan, Computational Frameworks for the Fast Fourier Transform, SIAM, Philadelphia (1992).

    Book  MATH  Google Scholar 

  6. M. K. Ng, R. H. Chan and W. C. Tang, “A fast algorithm for deblurring models with Neumann boundary conditions,” SIAM J. Sci. Comput., 21, 851–866 (1999).

    Article  MathSciNet  MATH  Google Scholar 

  7. S. Serra-Capizzano, “A note on antireflective boundary conditions and fast deblurring models,” SIAM J. Sci. Comput., 225, 1307–1325 (2003).

    MathSciNet  Google Scholar 

  8. A. Aricò, M. Donatelli and S. Serra-Capizzano, “Spectral analysis of the anti-reflective algebra,” Linear Algebra. Appl., 428, 657–675 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  9. L. Perrone, “Kronecker product approximations for image restoration with anti-reflective boundary conditions,” Numer. Linear Algebra Appl., 13, 1–22 (2006).

    Article  MathSciNet  MATH  Google Scholar 

  10. X. L. Zhao, T. Z. Huang, X. G. Lv, Z. B. Xu and J. Huang, “Kronecker product approximations for image restoration with new mean boundary conditions,” Appl. Math. Model., 36, 225–237 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  11. G. Aubert and L. Vese, “A variational method in image recovery,” SIAM J. Numer. Anal., 34, 1948–1979 (1997).

    Article  MathSciNet  MATH  Google Scholar 

  12. A. Chambolle and P. L. Lions, “Image recovery via total variation minimization and related problems,” Numer. Math., 76, 167–188 (1997).

    Article  MathSciNet  MATH  Google Scholar 

  13. L. Vese, Variational Problems and PDE’s for Image Analysis and Curve Evolution, PhD thesis, University of Nice (1996).

  14. L. I. Rudin, S. Osher and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D, 60, 259–268 (1992).

    Article  MathSciNet  MATH  Google Scholar 

  15. L. I. Rudin and S. Osher, “Total variation based image restoration with free local constraints,” Proc. IEEE Int. Conf. Imag. Proc., 1, 31–35 (1994).

    Article  Google Scholar 

  16. Y. Shi and Q. Chang, “Acceleration methods for image restoration problem with different boundary conditions,” Appl. Numer. Math., 58, 602–614 (2008).

    Article  MathSciNet  MATH  Google Scholar 

  17. Y. Shi and Q. S. Chang, “New model for image restoration with different boundary conditions,” Acta Math. Appl. Sinica Engl. Ser., 26, 369–380 (2010).

    Article  MathSciNet  MATH  Google Scholar 

  18. S. K. Lele, “Compact finite difference schemes with spectral-like resolution,” J. Comput. Phys., 103, 16–42 (1992).

    Article  MathSciNet  MATH  Google Scholar 

  19. M. Bastani and D. K. Salkuyeh, “A highly accurate method to solve Fisher’s equation,” Pramana J. Phys., 78, 335–346 (2012).

    Article  Google Scholar 

  20. G. Sutmann, “Compact finite difference schemes of sixth order for the Helmholtz equation,” J. Comput. Appl. Math., 203, 15–31 (2007).

    Article  MathSciNet  MATH  Google Scholar 

  21. M. Sari and G. Gürarslan, “A sixth-order compact finite difference scheme to the numerical solutions of Burgers’ equation,” Appl. Math. Comput., 208, 475–483 (2009).

    Article  MathSciNet  MATH  Google Scholar 

  22. S. Gottlieb, “On high order strong stability preserving Runge-Kutta and multi step time discretizations,” J. Sci. Comput., 25, 105–128 (2005).

    MathSciNet  MATH  Google Scholar 

  23. S. Gottlieb and C. W. Shu, “Total variation diminishing Runge-Kutta schemes,” Math. Comput., 221, 73–85 (1998).

    Article  MathSciNet  Google Scholar 

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Aghazadeh, N., Akbarifard, F. Combining Compact Finite Difference Schemes with Filters for Image Restoration. Comput Math Model 27, 206–216 (2016). https://doi.org/10.1007/s10598-016-9315-4

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  • DOI: https://doi.org/10.1007/s10598-016-9315-4

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