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.
Similar content being viewed by others
References
P. C. Hansen, J. G. Nagy and D. P. O’Leary, Deblurring Images: Matrices Spectra and Filtering, SIAM, Philadelphia (2006).
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition, Prentice Hall, New Jersey (2002).
A. K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliffs, NJ (1989).
T. F. Chan and J. H. Shen, Image Processing and Analysis: Variational, PDE, Wavelet and Stochastic Methods, SIAM, Philadelphia (2005).
C. V. Loan, Computational Frameworks for the Fast Fourier Transform, SIAM, Philadelphia (1992).
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).
S. Serra-Capizzano, “A note on antireflective boundary conditions and fast deblurring models,” SIAM J. Sci. Comput., 225, 1307–1325 (2003).
A. Aricò, M. Donatelli and S. Serra-Capizzano, “Spectral analysis of the anti-reflective algebra,” Linear Algebra. Appl., 428, 657–675 (2008).
L. Perrone, “Kronecker product approximations for image restoration with anti-reflective boundary conditions,” Numer. Linear Algebra Appl., 13, 1–22 (2006).
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).
G. Aubert and L. Vese, “A variational method in image recovery,” SIAM J. Numer. Anal., 34, 1948–1979 (1997).
A. Chambolle and P. L. Lions, “Image recovery via total variation minimization and related problems,” Numer. Math., 76, 167–188 (1997).
L. Vese, Variational Problems and PDE’s for Image Analysis and Curve Evolution, PhD thesis, University of Nice (1996).
L. I. Rudin, S. Osher and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D, 60, 259–268 (1992).
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).
Y. Shi and Q. Chang, “Acceleration methods for image restoration problem with different boundary conditions,” Appl. Numer. Math., 58, 602–614 (2008).
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).
S. K. Lele, “Compact finite difference schemes with spectral-like resolution,” J. Comput. Phys., 103, 16–42 (1992).
M. Bastani and D. K. Salkuyeh, “A highly accurate method to solve Fisher’s equation,” Pramana J. Phys., 78, 335–346 (2012).
G. Sutmann, “Compact finite difference schemes of sixth order for the Helmholtz equation,” J. Comput. Appl. Math., 203, 15–31 (2007).
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).
S. Gottlieb, “On high order strong stability preserving Runge-Kutta and multi step time discretizations,” J. Sci. Comput., 25, 105–128 (2005).
S. Gottlieb and C. W. Shu, “Total variation diminishing Runge-Kutta schemes,” Math. Comput., 221, 73–85 (1998).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10598-016-9315-4