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Inverse Polynomial Reconstruction of Two Dimensional Fourier Images

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Abstract

The Gibbs phenomenon is intrinsic to the Fourier representation for discontinous problems. The inverse polynomial reconstruction method (IPRM) was proposed for the resolution of the Gibbs phenomenon in previous papers [Shizgal, B. D., and Jung, J.-H. (2003) and Jung, J.-H., and Shizgal, B. D. (2004)] providing spectral convergence for one dimensional global and local reconstructions. The inverse method involves the expansion of the unknown function in polynomials such that the residue between the Fourier representations of the final representation and the unknown function is orthogonal to the Fourier or polynomial spaces. The main goal of this work is to show that the one dimensional inverse method can be applied successfully to reconstruct two dimensional Fourier images. The two dimensional reconstruction is implemented globally with high accuracy when the function is analytic inside the given domain. If the function is piecewise analytic and the local reconstruction is sought, the inverse method is applied slice by slice. That is, the one dimensional inverse method is applied to remove the Gibbs oscillations in one direction and then it is applied in the other direction to remove the remaining Gibbs oscillations. It is shown that the inverse method is exact if the two-dimensional function to be reconstructed is a piecewise polynomial. The two-dimensional Shepp–Logan phantom image of the human brain is used as a preliminary study of the inverse method for two dimensional Fourier image reconstruction. The image is reconstructed with high accuracy with the inverse method

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References

  1. R. Archibald A. Gelb (2002) ArticleTitleReducing the effects of noise in image reconstruction J. Sci. Comp. 17 167–180 Occurrence Handle2003d:94006

    MathSciNet  Google Scholar 

  2. Archibald R., Chen K., Gelb A., Renaut R. (2003). Improving tissue segmentation of human brain MRI through pre-processing by the Gegenbauer reconstruction method. Neuroimage, to appear.

  3. H. Bateman (1953) Higher Transcendental Functions Vol 2 McGraw-Hill New York

    Google Scholar 

  4. C. Canuto M.Y. Hussaini A. Quarteroni T.A. Zang (1988) Spectral Methods in Fluid Dynamics, Springer Series in Computational Physics Springer New York

    Google Scholar 

  5. H. Chen B.D. Shizgal (2001) ArticleTitleA spectral solution of the Sturm-Liouville equation: comparison of classical and nonclassical basis sets J. Comp. Appl. Math. 136 17–35 Occurrence Handle10.1016/S0377-0427(00)00573-2 Occurrence Handle2002g:65088

    Article  MathSciNet  Google Scholar 

  6. P.J. Davis P. Rabinowitz (1989) Methods of Numerical Integration Academic Press New York

    Google Scholar 

  7. T.A. Driscoll B. Fornberg (2001) ArticleTitleA Padé-based algorithm for overcoming the Gibbs phenomenon Numerical Algorithms 26 77–92 Occurrence Handle10.1023/A:1016648530648 Occurrence Handle2002b:65007

    Article  MathSciNet  Google Scholar 

  8. M.P. Ekstrom (1984) Digital Image Processing Techniques, Computational Techniques vol 2 Academic Press Orlando

    Google Scholar 

  9. H.G. Feichtinger T. Strohmer (2001) Numerical Harmonic Analysis and Image Processing W.G. Kropatsch H Bischof (Eds) Digital Image Analysis Springer-Verlag New York 7–47

    Google Scholar 

  10. W. Gautschi (1985) J. Comp. Appl. Math. 12 61–76 Occurrence Handle10.1016/0377-0427(85)90007-X Occurrence Handle0583.65011 Occurrence Handle87a:65045

    Article  MATH  MathSciNet  Google Scholar 

  11. A. Gelb D. Gottlieb (1997) ArticleTitleThe resolution of the Gibbs phenomenon for “sliced” functions in one and two dimensions Comp. Math. Appl. 33 35–38 Occurrence Handle10.1016/S0898-1221(97)00086-2 Occurrence Handle98c:65206

    Article  MathSciNet  Google Scholar 

  12. G.H. Golub C.F. Loan ParticleVan (1996) Matrix Computations EditionNumber3 Johns Hopkins UP Baltimore

    Google Scholar 

  13. R.C. Gonzalez R.E. Woods (1992) Digital Image Processing Wesley Publishing Addison

    Google Scholar 

  14. D. Gottlieb J.S. Hesthaven (2001) ArticleTitleSpectral methods for hyperbolic problems J. Comp. Appl. Math. 128 83–131 Occurrence Handle10.1016/S0377-0427(00)00510-0 Occurrence Handle2001m:65138

    Article  MathSciNet  Google Scholar 

  15. D. Gottlieb S. Orszag (1977) Numerical Analysis of Spectral Methods: Theory and Applications SIAM Philadelphia

    Google Scholar 

  16. D. Gottlieb C.-W. Shu (1997) ArticleTitleOn the Gibbs phenomenon and its resolution SIAM Rev. 39 644–668 Occurrence Handle10.1137/S0036144596301390 Occurrence Handle98m:42002

    Article  MathSciNet  Google Scholar 

  17. D. Gottlieb C.-W. Shu A. Solomonoff H. Vandeven (1992) ArticleTitleOn the Gibbs phenomenon I: Recovering exponential accuracy from the Fourier partial sum of a nonperiodic analytic function J. Comput. Appl. Math. 43 81–92 Occurrence Handle10.1016/0377-0427(92)90260-5 Occurrence Handle94h:42006

    Article  MathSciNet  Google Scholar 

  18. Gottlieb, D., and Tadmor, E. (1985). Recovering pointwise values of discontinuous data within spectral accuracy. In Progress and Supercomputing in Computational Fluid Dynamics, Murman, E.M., and Abarbanel, S.S.(eds), Proceedings of 1984 U.S.-Israel Workshop, Progress in Scientific Computing, vol. 6, Birkhauser, Boston, 357–375

  19. I.S. Gradshteyn I.M. Ryzhik (200)) Table of Integrals, Series, and Products EditionNumber6 Academic Press San Diego

    Google Scholar 

  20. W.B. Green (1989) Digital Image Processing: A Systems Approach EditionNumber2 Van Nostrand Reinhold New York

    Google Scholar 

  21. R.M. Hord (1982) Digital Image Processing of Remotely Sensed Data, Series of Notes and Reports in Computer Science and Applied Mathematics Academic Press New York

    Google Scholar 

  22. Hu M.-K. (1962). Visual pattern recognition by moment invariants. IRE Trans. on Information Theory IT-8, 179–187

    Google Scholar 

  23. J.-H. Jung B.D. Shizgal (2004) ArticleTitleGeneralization of the inverse polynomial reconstruction method in the Resolution of the Gibbs Phenomena J. Comp. Appl. Math. 172 131–151 Occurrence Handle10.1016/j.cam.2004.02.003 Occurrence Handle2005d:42002

    Article  MathSciNet  Google Scholar 

  24. A.C. Kak (1984) Image reconstruction from projections M.P. Ekstrom (Eds) Digital Image Processing Techniques Academic Press New York 111–170

    Google Scholar 

  25. D. Lanczos (1996) Discourse on Fourier series Hafner Publishing Company New York

    Google Scholar 

  26. A.V. Oppenheim A.S. Wilsky S.H. Nawab (1996) Signals and Systems EditionNumber2 Prentice Hall New Jersey

    Google Scholar 

  27. W.K. Pratt (1991) Digital Image Processing EditionNumber2 John Wiley and Sons New York

    Google Scholar 

  28. S.W. Rowland (1979) Computer Implementation of Image Reconstruction Formulas G.T. Herman (Eds) Image Reconstruction from Projection Topics in Applied Physics, Vol. 32 Springer-Verlag New York 9–79

    Google Scholar 

  29. L.A. Shepp B.F. Logan (1974) ArticleTitleThe Fourier reconstruction of a head section IEEE Trans Nucl. Sci NS. 21 21–43

    Google Scholar 

  30. B.D. Shizgal J.-H. Jung (2003) ArticleTitleTowards the resolution of the Gibbs phenomena J. Comp. Appl. Math. 161 41–65 Occurrence Handle10.1016/S0377-0427(03)00500-4 Occurrence Handle2004k:42042

    Article  MathSciNet  Google Scholar 

  31. E. Tadmor J. Tanner (2002) ArticleTitleAdaptive mollifiers for high resolution recovery of piecewise smooth data from its spectral information Found. Comp. Math. 2 155–189 Occurrence Handle2003b:42009

    MathSciNet  Google Scholar 

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Correspondence to Jae-Hun Jung.

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Jung, JH., Shizgal, B.D. Inverse Polynomial Reconstruction of Two Dimensional Fourier Images. J Sci Comput 25, 367–399 (2005). https://doi.org/10.1007/s10915-004-4795-3

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  • DOI: https://doi.org/10.1007/s10915-004-4795-3

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