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Semidiscrete and Discrete Well-Posedness of Shock Filtering

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Mathematical Morphology: 40 Years On

Part of the book series: Computational Imaging and Vision ((CIVI,volume 30))

Abstract

While shock filters are popular morphological image enhancement methods, no well-posedness theory is available for their corresponding partial differential equations (PDEs). By analysing the dynamical system of ordinary differential equations that results from a space discretisation of a PDE for 1-D shock filtering, we derive an analytical solution and prove well-posedness. Finally we show that the results carry over to the fully discrete case when an explicit time discretisation is applied.

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References

  1. L. Alvarez and L. Mazorra. Signal and image restoration using shock filters and anisotropic diffusion. SIAM Journal on Numerical Analysis, 31:590–605, 1994.

    Article  Google Scholar 

  2. R. W. Brockett and P. Maragos. Evolution equations for continuous-scale morphology. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, volume 3, pages 125–128, San Francisco, CA, March 1992.

    Google Scholar 

  3. G. Gilboa, N. A. Sochen, and Y. Y. Zeevi. Regularized shock filters and complex diffusion. In A. Heyden, G. Sparr, M. Nielsen, and P Johansen, editors, Computer Vision — ECCV2002, volume 2350 of Lecture Notes in Computer Science, pages 399–413. Springer, Berlin, 2002.

    Google Scholar 

  4. F. Guichard and J.-M. Morel. A note on two classical shock filters and their asymptotics. In M. Kerckhove, editor, Scale-Space and Morphology in Computer Vision, volume 2106 of Lecture Notes in Computer Science, pages 75–84. Springer, Berlin, 2001.

    Google Scholar 

  5. P. Kornprobst, R. Deriche, and G. Aubert. Nonlinear operators in image restoration. In Proc. 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 325–330, San Juan, Puerto Rico, June 1997. IEEE Computer Society Press.

    Google Scholar 

  6. H. P. Kramer and J. B. Bruckner. Iterations of a non-linear transformation for enhancement of digital images. Pattern Recognition, 7:53–58, 1975.

    Article  Google Scholar 

  7. S. Osher and L. Rudin. Shocks and other nonlinear filtering applied to image processing. In A. G. Tescher, editor, Applications of Digital Image Processing XIV, volume 1567 of Proceedings of SPIE, pages 414–431. SPIE Press, Bellingham, 1991.

    Google Scholar 

  8. S. Osher and L. I. Rudin. Feature-oriented image enhancement using shock filters. SIAM Journal on Numerical Analysis, 27:919–940, 1990.

    Article  Google Scholar 

  9. S. Osher and J. A. Sethian. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79:12–49, 1988.

    Article  Google Scholar 

  10. I. Pollak, A. S. Willsky, and H. Krim. Image segmentation and edge enhancement with stabilized inverse diffusion equations. IEEE Transactions on Image Processing, 9(2):256–266, February 2000.

    Article  Google Scholar 

  11. L. Remaki and M. Cheriet. Numerical schemes of shock filter models for image enhancement and restoration. Journal of Mathematical Imaging and Vision, 18(2):153–160, March 2003.

    Article  Google Scholar 

  12. J. G. M. Schavemaker, M. J. T. Reinders, J. J. Gerbrands, and E. Backer. Image sharpening by morphological filtering. Pattern Recognition, 33:997–1012, 2000.

    Article  Google Scholar 

  13. J. Weickert. Anisotropic Diffusion in Image Processing. Teubner, Stuttgart, 1998.

    Google Scholar 

  14. J. Weickert. Coherence-enhancing shock filters. In B. Michaelis and G. Krell, editors, Pattern Recognition, volume 2781 of Lecture Notes in Computer Science, pages 1–8, Berlin, 2003. Springer.

    Google Scholar 

  15. M. Welk, J. Weickert, I. Galié. Theoretical Foundations for 1-D Shock Filtering. Preprint, Saarland University, Saarbruecken, 2005.

    Google Scholar 

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Welk, M., Weickert, J. (2005). Semidiscrete and Discrete Well-Posedness of Shock Filtering. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_28

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  • DOI: https://doi.org/10.1007/1-4020-3443-1_28

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-1-4020-3442-8

  • Online ISBN: 978-1-4020-3443-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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