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Nonlinear Projection Recovery in Digital Inpainting for Color Image Restoration

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Abstract

This paper concerns with nonuniform sampling and interpolation methods combined with variational models for the solution of a generalized color image inpainting problem and the restoration of digital signals. In particular, we discuss the problem of reconstructing a digital signal/image from very few, sparse, and complete information and from a substantially incomplete information, which will be assumed as the result of a nonlinear distortion. Differently from well known inpainting applications for the recovery of gray images, the proposed techniques apply to color images embedding blanks where only gray level information is given. As a typical and inspiring example, we illustrate the concrete problem of the color restoration of a destroyed art fresco from its few known fragments and some gray picture taken prior to the damage. Numerical implementations are included together with several examples and numerical results to illustrate the proposed method. The numerical experience suggests furthermore that a particular system of coupled Hamilton-Jacobi equations is well-posed.

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References

  1. A. Aldroubi and K. Gröchenig, “Nonuniform sampling and reconstruction in shift-invariant spaces”, SIAM Review, Vol. 43, No. 4, pp. 585–620, 2001.

    Article  MathSciNet  Google Scholar 

  2. L. Alvarez, P.-L. Lions and J.-M. Morel, “Image selective smoothing and edge detection by nonlinear diffusion. II”, SIAM J. Numer. Anal., Vol. 29, No. 3, pp. 845–866, 1992.

    Article  MathSciNet  Google Scholar 

  3. L. Ambrosio and S. Masnou, “A direct variational approach to a problem arising in image reconstruction”, Interface and Free Boundaries, Vol. 5, pp. 63–81, 2003.

    MathSciNet  Google Scholar 

  4. G. Aubert and P. Kornprobst, Mathematical Problems in Image Processing. Partial Differential Equations and the Calculus of Variations, Springer, 2002.

  5. R. F. Bass and K. Gröchenig, “Random sampling of multivariate trigonometric polynomials”, SIAM J. Math. Anal., Vol. 36, No. 3, pp. 773–795, 2004.

    Article  MathSciNet  Google Scholar 

  6. M. Beltramio, G. Sapiro, V. Caselles and B. Ballester, “Image inpainting”, SIGGRAPH 2000, July, 2001.

  7. J. J. Benedetto and P. J. S. G. Ferreira, Modern Sampling Theory: Mathematics and Applications, Birkhäuser, 2000.

  8. J. J. Benedetto and M. Frezier, Wavelets: Mathematics and Applications, CRC Press, 1993.

  9. P. Binev, A. Cohen, W. Dahmen, R. DeVore, and V. Temlyakov, “Universal algorithms for learning theory - Part I: piecewise constant functions”, Report, IGPM, RWTH Aachen, 2004.

  10. G. Buttazzo and A. Visintin (Eds.), Motion by Mean Curvature and Related Topics: Proceedings of the International Conference Held at Trento, Italy, July 20–24, 1992, Berlin: de Gruyter. viii, 1994.

  11. T. F. Chan and J. Shen, “Inpainting based on nonlinear transport and diffusion”, Contemp. Math., Vol. 313, pp. 53–65, 2002.

    MathSciNet  Google Scholar 

  12. F. Cucker and S. Smale, “On the mathematical foundations of learning”, Bull. Am. Math. Soc., New Ser., Vol. 39, No. 1, pp. 1–49, 2002.

    Article  MathSciNet  Google Scholar 

  13. L. C. Evans, “Partial Differential Equations”, Graduate Studies in Mathematics, 19. Providence, RI: American Mathematical Society (AMS). xvii, 1998.

  14. L. C. Evans and J. Spruck, “Motion of level sets by mean curvature. I”, J. Differ. Geom., Vol. 33, No. 3, pp. 635–681, 1991.

    MathSciNet  Google Scholar 

  15. H. G. Feichtinger and K. Gröchenig, “Iterative reconstruction of multivariate band-limited functions from irregular sampling values”, SIAM J. Math. Anal., Vol. 1, pp. 244–261, 1992.

    Article  Google Scholar 

  16. H. G. Feichtinger, K. Gröchenig and T. Strohmer, “Efficient numerical methods in non-uniform sampling theory”, Numer. Math., Vol. 69, pp. 423–440, 1995.

    Article  MathSciNet  Google Scholar 

  17. H. G. Feichtinger and T. Strohmer, “Recovery missing segments and lines in images”, Opt. Eng. special issue on Digital Image Recovery and Synthesis, Vol. 33, No. 10, pp. 3283–3289, 1994.

    Google Scholar 

  18. H. G. Feichtinger and T. Strohmer (Eds.), Gabor Analysis and Algorithms, Birkhäuser, 1998.

  19. H. G. Feichtinger and T. Strohmer (Eds.), Advances in Gabor Analysis, Birkhäuser, 2003.

  20. M. Fornasier and D. Toniolo, “Compactly supported circular harmonics, fast, robust and efficient 2D pattern recognition”, technical report DFPD 02/EI/32, Dept. Physics “G. Galilei”, University of Padova, 2002.

  21. M. Fornasier and D. Toniolo, “Computer-based recomposition of the frescoes in the Ovetari Chapel in the Church of the Eremitani in Padua. Methodology and initial results, (English/Italian)”, in Mantegna Nella Chiesa Degli Eremitani a Padova. Il recupero possibile, Ed. Skira, 2003.

  22. M. Fornasier and D. Toniolo, “Fast, robust, and efficient 2D pattern recognition for re-assembing fragmented digital images”, (2005), to appear in Pattern Recognition.

  23. K. Gröchenig, Foundations of Time-Frequency Analysis, Birkhäuser, 2000.

  24. H. Ishii and S. Koike, “Viscosity solutions for monotone systems of second order elliptic PDEs”, Commun. Partial Differ. Equations, Vol. 16, No. 6/7, pp. 1095–1128, 1991.

    MathSciNet  Google Scholar 

  25. M. Katsoulakis and S. Koike, “Viscosity solutions of monotone systems for Dirichlet problems”, Differ. Integral Equ., Vol. 7, No. 2, pp. 367–382, 1994.

    MathSciNet  Google Scholar 

  26. S. Koike, “Uniqueness of viscosity solutions for monotone systems of fully nonlinear PDEs under Dirichlet condition”, Nonlinear Anal., Theory Methods Appl., Vol. 22, No. 4, pp. 519–532, 1994.

    Article  MATH  MathSciNet  Google Scholar 

  27. G. Küne, J. Weickert, M. Beier and W. Effelsberg, “Fast implicit active contour models”, DAGM 2002, LNCS 2449 (L. Van Gool, Ed.), Springer-Verlag Berlin Heidelberg, 2002, pp. 133–140.

  28. S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, 1998

  29. G. Dal Maso, An Introduction to Γ-Convergence, Birkhäuser, Boston, 1993.

    Google Scholar 

  30. A. M. Oberman, “A convergent monotone difference scheme for motion of level sets by mean curvature”, Numer. Math., Vol. 99, pp. 365–379, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  31. L. Vese, “A study in the BV space of a denoising-deblurring variational problem”, Appl. Math. Optim., Vol. 44, pp. 131–161, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  32. G. Winkler, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. A Mathematical Introdution. 2nd Revised Ed., Applications of Mathematics. 27. Berlin: Springer, 2003.

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Correspondence to Massimo Fornasier.

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Massimo Fornasier received his Ph.D. degree in Computational Mathematics on February 2003 at the University of Padova, Italy. Within the European network RTN HASSIP (Harmonic Analysis and Statistics for Signal and Image Processing) HPRN-CT-2002-00285, he cooperated as PostDoc with NuHAG (the Numerical Harmonic Analysis Group), Faculty of Mathematics of the University of Vienna, Austria and the AG Numerical/Wavelet-Analysis Group of the Department of Mathematics and Computer Science of the Philipps-University in Marburg, Germany (2003). Since June 2003 he is research assistant at the Department of Mathematical Methods and Models for the Applied Science at the University of Rome “La Sapienza”. Since May 2004 he is Individual Marie Curie Fellow (project FTFDORF-FP6-501018) at NuHAG. His research interests include applied harmonic analysis with particular emphasis on time-frequency analysis and decompositions for applications in signal and image processing. Since 1998, he developed with Domenico Toniolo the Mantegna Project (http://www.pd.infn.it/~labmante/) at the University of Padova and the local laboratory for image processing and applications in art restoration. Recently he has focused his attention on adaptive and dynamical schemes for the numerical solution of (pseudo) differential equations and inverse problems in digital signal processing.

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Fornasier, M. Nonlinear Projection Recovery in Digital Inpainting for Color Image Restoration. J Math Imaging Vis 24, 359–373 (2006). https://doi.org/10.1007/s10851-006-4242-1

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