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Adaptive cell-centered finite volume method for diffusion equations on a consistent quadtree grid

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Abstract

Models applied in image processing are often described by nonlinear PDEs in which a good approximation of gradient plays an important role especially in such cases where irregular finite volume grids are used. In image processing, such a situation can occur during a coarsening based on quadtree grids. We present a construction of a deformed quadtree grid in which the connection of representative points of two adjacent finite volumes is perpendicular to their common boundary enabling us to apply the classical finite volume methods. On the other hand, for such an adjusted grid, the intersection of representative points connection with a finite volume boundary is not a middle point of their common edge and standard methods cannot achieve a good accuracy. In this paper we present a new cell-centered finite volume method to evaluate solution gradients, which results into a solution of a simple linear algebraic system and we prove its unique solvability. Finally we present numerical experiments for the regularized Perona-Malik model in which we applied this new method.

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References

  1. Aftosmis, M.J., Berger, M.J., Melton, J.R.: Adaptive Cartesian mesh generation. Chapter 22 in Handbook of Grid Generation. CRC Press (1998)

  2. Alvarez, L., Guichard, F., Lions, P.L., Morel, J.M.: Axioms and fundamental equations of image processing. Archive Rat. Mech. Anal. 123, 200–257 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bänsch, E., Mikula, K.: A coarsening finite element strategy in image selective smoothing. Comput. Visual. Sci. 1(1), 53–61 (1997)

    Article  MATH  Google Scholar 

  4. Bänsch, E., Mikula, K.: Adaptivity in 3D image processing. Comput. Visual. Sci. 4(1), 21–30 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, Z., Wu, J., Xu, Y.: Higher-order finite volume methods for elliptic boundary value problems. Adv. Comput. Math. 37, 191–253 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  6. Chen, Z., Xu, Y., Zhang, Y.: A construction of higher-order finite volume methods. Math. Comput. S0025–5718, 02891–3 (2014)

    MathSciNet  Google Scholar 

  7. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. ICCV, 694–699 (1995)

  8. Catté, F., Lions, P.L., Morel, J.M., Coll, T.: and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 129, 182–193 (1992)

    Article  MATH  Google Scholar 

  9. Coudiére, Y., Vila, J.P., Villedieu, P.: Convergence rate of a finite volume scheme for the linear convection-diffusion equation on locally refined meshes. ESAIM: M2AN 34(6), 1123–1149 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  10. Droske, M., Meyer, B., Rumpf, M., Schaller, C.: An Adaptive level set method for medical image segmentation. Lect. Notes Comput. Sci 4(2001), 416–422 (2082)

    MATH  Google Scholar 

  11. Eymard, R., Gallouet, T., Herbin, R.: The finite volume method. In: Ciarlet, Ph., Lions, J. L. (eds.) Handbook for Numerical Analysis, vol. 7. Elsevier (2000)

  12. Eymard, R., Handlovicová, A., Mikula, K.: Study of a finite volume scheme for the regularized mean curvature flow level set equation. IMA J. Numer. Anal 31(3), 813–846 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  13. Chen, H., Min, Ch., Gibou, F.: A supra-convergent finite difference scheme for the poisson and heat equations on irregular domains and non-graded Cartesian Grids. J. Sci. Comput 31(1/2), 19–59 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  14. Johansen, H., Colella, P.: A cartesian grid embedded boundary method for Poisson’s equation on irregular domains. J. Comput. Phys. 147(60), 60–85 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  15. Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A.: Conformal curvature flows: from phase transitions to active vision. Arch. Rational Mech. Anal 134, 275–301 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  16. Krivá, Z.: Adaptive finite volume methods in image processing. Edicia vedeckých prác, Zošit c. 15, Vydavatelstvo STU Bratislava (2004)

  17. Krivá, Z., Mikula, K.: An adaptive finite volume scheme for solving nonlinear diffusion equations in image processing. J. Vis. Commun. Image Represent 13, 22–35 (2002)

    Article  Google Scholar 

  18. Krivá, Z., Mikula, K.: Adaptive diamond cell finite volume method in image processing. Proc. ALGORITMY 2009, Conf. on Scientific Computing, pp. 174–188. Podbanské (2009)

  19. Min, Ch., Gibou, F.: A second order accurate level set method on non-graded adaptive cartesian grids. In: Journal of Computational Physics, vol. 225, pp. 300–321. Elsevier (2007)

  20. Mikula, K., Ramarosy, N.: Semi-implicit finite volume scheme for solving nonlinear diffusion equations in image processing. Numerische Mathematik 89(3), 561–590 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  21. Ohlberger, M., Rumpf, M.: Adaptive projection operators in multiresolutional scientific visualization. IEEE Trans. Visual. Comput. Graph. 4(4), 344–364 (1998)

    Article  Google Scholar 

  22. Perona, P.: J. Scale space and edge detection using anisotropic diffusion. In: Proc. IEEE Computer Society Workshop on Computer Vision, Malik (1987)

  23. Preusser, T., Rumpf, M.: An adaptive finite element method for large scale image processing. J. Vis. Commun. Image Represent. 11(2), 183–195 (2000)

    Article  Google Scholar 

  24. Preusser, T., Rumpf, M.: A level set method for anisotropic geometric diffusion in 3D image processing. SIAM J. Appl.Math. 62(5), 1772–1793 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  25. Samet, H.: Application of spatial data structures: Computer Graphics, Image Processing and GIS. Adison Wesley, New York (1990)

    Google Scholar 

  26. Samet, H.: The design and analysis of spatial data structures. Adison Wesley, New York (1989)

    Google Scholar 

  27. Sethian, J.A.: Level Set Methods and Fast Marching Methods. Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision and Material Science. Cambridge University Press, New York (1999)

    MATH  Google Scholar 

  28. Vemuri, B., Chen, Y., Wang, Z.: Registration assisted image smoothing and segmentation. ECCV 4, 546–559 (2002)

    MATH  Google Scholar 

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Correspondence to Zuzana Krivá.

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Communicated by: Y. Xu

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Krivá, Z., Handlovičová, A. & Mikula, K. Adaptive cell-centered finite volume method for diffusion equations on a consistent quadtree grid. Adv Comput Math 42, 249–277 (2016). https://doi.org/10.1007/s10444-015-9423-2

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  • DOI: https://doi.org/10.1007/s10444-015-9423-2

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