Skip to main content
Log in

TV Based Image Restoration with Local Constraints

  • Published:
Journal of Scientific Computing Aims and scope Submit manuscript

Abstract

The problem of recovering an image that has been blurred and corrupted with additive noise is ill-posed. Among the methods that have been proposed to solve this problem, one of the most successful ones is that of constrained Total Variation (TV) image restoration, proposed by L. Rudin, S. Osher, and E. Fatemi. In its original formulation, to ensure the satisfaction of constraints, TV restoration requires the estimation of a global parameter λ (a Lagrange multiplier). We observe that if λ is global, the constraints of the method are also satisfied globally, but not locally. The effect is that the restoration is better achieved in some regions of the image than in others. To avoid this, we propose a variant of the TV restoration model including, instead of a single constraint λ, a set of constraints λ i , each one corresponding to a region O i of the image. We discuss the existence and uniqueness of solutions of the proposed model and display some numerical experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Acar, R., and Vogel, C. R. (1994). Analysis of total variation penalty methods for Ill-posed problems. Inverse Problems 10, 1217-1229.

    Google Scholar 

  2. Alvarez, L., Gousseau, Y., and Morel, J. M. (1999). The size of objects in natural and artificial images. Advances in Imaging and Electron Physics 111.

  3. Ambrosio, L., Fusco, N., and Pallara, D. (2000). Functions of Bounded Variation and Free Discontinuity Problems, Oxford Mathematical Monographs.

  4. Andreu, F., Ballester, C., Caselles, V., and Mazón, J. M. (2001). Minimizing total variation flow. Differential Integral Equations 14, 321-360.

    Google Scholar 

  5. Anzellotti, G. (1983). Pairings between measures and bounded functions and compensated compactness. Ann. di Matematica Pura ed Appl. IV 135, 293-318.

    Google Scholar 

  6. Bellettini, G., Caselles, V., and Novaga, M. The Total Variation Flow in ℝN, Preprint 2001.

  7. Black, M., and Sapiro, G. (1999). Edges as Outliers: Anisotropic Smoothing using Local Image Statistics, Proceedings Scale-Space Conference, Corfu, Greece.

  8. Chambolle, A., and Lions, P. L. (1995). Image Recovery via Total Variation Minimization and Related Problems, Preprint.

  9. Chan, T. F., Golub, G. H., and Mulet, P. (1999). A nonlinear primal-dual method for total variation based image restoration. SIAM J. Sci. Comput. 20(6), 1964-1977.

    Google Scholar 

  10. Chan, T. F., Golub, G. H., and Mulet, P. (1997). Total Variation Image Restoration: Numerical Methods and Extensions, Proceedings International Conference on Image Processing, ICIP-97, October 26-29, Santa Barbara, California, Vol. III, pp. 384-387.

  11. Ciarlet, P. G. (1988). Introduction to Numerical Linear Algebra and Optimization, Cambridge University Press.

  12. Demoment, G. (1989). Image reconstruction and restoration: Overview of common estimation structures and problems, IEEE Trans. on Acoustics, Speech and Signal Proc. 37(12), 2024-2036.

    Google Scholar 

  13. Donoho, D. L., Johnstone, I. M., Kerkyacharian, G., and Picard, D. (1995). Wavelet Shrinkage: Asymptopia? J. Roy. Statist. Soc. Ser. B 57, 301-369.

    Google Scholar 

  14. Durand, S., Malgouyres, F., and Rougé, B. (1999). Image Deblurring, Spectrum Interpolation and Application to Satellite Imaging, Mathematical Modelling and Numerical Analysis.

  15. Evans, L. C., and Gariepy, R. F. (1992). Measure Theory and Fine Properties of Functions, Studies in Advanced Math., CRC Press.

  16. Geman, D., and Reynolds, G. (1992). Constrained image restoration and recovery of discontinuities. IEEE Trans. Pattern Anal. Machine Intell. 14, 367-383.

    Google Scholar 

  17. Groetsch, C. W. (1984). The Theory of Tikhonov Regularization for Fredholm Integral Equations of the First Kind, Pitman, Boston.

    Google Scholar 

  18. Ito, K., and Kunisch, K. (1999). An Active Set Strategy Based On The Augmented Lagrangian Formulation For Image Restoration, Mathematical Modelling and Numerical Analysis, Vol. 33, No. 1, pp. 1-21

    Google Scholar 

  19. Koepfler, G., Lopez, C., and Morel, J.-M. (1994). A multiscale algorithm for image segmentation by variational method. SIAM J. Numer. Anal. 31, 282-299.

    Google Scholar 

  20. Lions, P. L., Osher S., and Rudin, L. (1992). Denoising and Deblurring using Constrained Nonlinear Partial Differential Equations, Tech. Repport, Cognitech, Santa Monica, CA, submitted to SINUM.

    Google Scholar 

  21. Moisan, L. (2001). Extrapolation de spectre et variation totale ponderée, Preprint.

  22. Morel, J. M., and Solimini, S. (1994). Variational Methods in Image Processing, Birkhäuser.

  23. Mumford, D., and Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math., 17, 577-685.

    Google Scholar 

  24. Nikolova, M. (2000). Local strong homogeneity of a regularized estimator. SIAM J. Appl. Math 61, 633-658.

    Google Scholar 

  25. Peressini, A. L., Sullivan, F. E., Uhl, J. J., Jr. (1988). The Mathematics of Nonlinear Programming, Springer Verlag.

  26. Rohatgi, V. K. (1976). An Introduction to Probability Theory and Mathematical Statistics, Wiley.

  27. Rougé, B. (1998). Théorie de l'échantillonage et satellites d'observation de la terre, Analyse de Fourier et traitement d'images, Journées X-UPS.

  28. Rosen, J. G. (1961). The gradient projection method for nonlinear programming. Part II. Nonlinear constraints. J. Soc. Indust. Appl. Math. 9, 514-532.

    Google Scholar 

  29. Rudin, L. I. (1987). Images, Numerical Analysis of Singularities and Shock Filters, Ph.D. dissertation, Caltech, Pasadena, California n5250:TR.

    Google Scholar 

  30. Rudin, L., and Osher, S. (1994). Total Variation based Image Restoration with Free Local Constraints, Proc. of the IEEE ICIP-94, Vol. 1, Austin, TX, pp. 31-35.

  31. Rudin, L. I., Osher, S., and Fatemi, E. (1992). Nonlinear total variation based noise removal algorithms. Phys. D 60, 259-269.

    Google Scholar 

  32. Strong, D., Blomgren, P., and Chan, T. Spatially Adaptative Local Feature Driven Total Variation Minimizing Image Restoration, CAM Report.

  33. Strong, D., and Chan, T. F. (1996). Spatially and Adaptative Total Variation Based Regularization and Anisotropic Diffusion in Image Processing, CAM Report, UCLA.

  34. Tikhonov, A. N., and Arsenin, V. Y. (1977). Solutions of Ill-Posed Problems, John Wiley, New York.

    Google Scholar 

  35. Twomey, S. (1965). The application of numerical filtering to the solution of integral equations encountered in indirect sensing measurements. J. Franklin Inst. 297, 95-109.

    Google Scholar 

  36. Vogel, C. R., and Oman, M. E. (1996). Iterative methods for total variation denoising. SIAM J. Sci. Computing 17(1), 227-238.

    Google Scholar 

  37. Vogel, C. R., and Oman, M. E. (1995). Fast Total Variation Based Image Reconstruction, Proceedings of the 1995 ASME Design Engineering Conferences, Vol. 3, pp. 1009-1015.

    Google Scholar 

  38. Ziemer, W. P. (1989). Weakly Differentiable Functions, GTM 120, Springer-Verlag.

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bertalmio, M., Caselles, V., Rougé, B. et al. TV Based Image Restoration with Local Constraints. Journal of Scientific Computing 19, 95–122 (2003). https://doi.org/10.1023/A:1025391506181

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1025391506181

Navigation