Skip to main content
Erschienen in: Journal of Scientific Computing 2-3/2013

01.02.2013

Bregmanized Domain Decomposition for Image Restoration

verfasst von: Andreas Langer, Stanley Osher, Carola-Bibiane Schönlieb

Erschienen in: Journal of Scientific Computing | Ausgabe 2-3/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Computational problems of large-scale data are gaining attention recently due to better hardware and hence, higher dimensionality of images and data sets acquired in applications. In the last couple of years non-smooth minimization problems such as total variation minimization became increasingly important for the solution of these tasks. While being favorable due to the improved enhancement of images compared to smooth imaging approaches, non-smooth minimization problems typically scale badly with the dimension of the data. Hence, for large imaging problems solved by total variation minimization domain decomposition algorithms have been proposed, aiming to split one large problem into N>1 smaller problems which can be solved on parallel CPUs. The N subproblems constitute constrained minimization problems, where the constraint enforces the support of the minimizer to be the respective subdomain.
In this paper we discuss a fast computational algorithm to solve domain decomposition for total variation minimization. In particular, we accelerate the computation of the subproblems by nested Bregman iterations. We propose a Bregmanized Operator Splitting–Split Bregman (BOS-SB) algorithm, which enforces the restriction onto the respective subdomain by a Bregman iteration that is subsequently solved by a Split Bregman strategy. The computational performance of this new approach is discussed for its application to image inpainting and image deblurring. It turns out that the proposed new solution technique is up to three times faster than the iterative algorithm currently used in domain decomposition methods for total variation minimization.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Ambrosio, L., Fusco, N., Pallara, D.: Functions of Bounded Variation and Free Discontinuity Problems. Oxford Mathematical Monographs. Clarendon Press, Oxford (2000), p. xviii MATH Ambrosio, L., Fusco, N., Pallara, D.: Functions of Bounded Variation and Free Discontinuity Problems. Oxford Mathematical Monographs. Clarendon Press, Oxford (2000), p. xviii MATH
2.
Zurück zum Zitat Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing. Partial Differential Equations and the Calculus of Variation. Springer, Berlin (2002) Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing. Partial Differential Equations and the Calculus of Variation. Springer, Berlin (2002)
3.
Zurück zum Zitat Bregman, L.: The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex optimization. U.S.S.R. Comput. Math. Math. Phys. 7, 200–217 (1967) CrossRef Bregman, L.: The relaxation method of finding the common points of convex sets and its application to the solution of problems in convex optimization. U.S.S.R. Comput. Math. Math. Phys. 7, 200–217 (1967) CrossRef
4.
Zurück zum Zitat Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20(1–2), 89–97 (2004) MathSciNet Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20(1–2), 89–97 (2004) MathSciNet
5.
Zurück zum Zitat Chambolle, A., Darbon, J.: On total variation minimization and surface evolution using parametric maximum flows. Int. J. Comput. Vis. 84, 288–307 (2009) CrossRef Chambolle, A., Darbon, J.: On total variation minimization and surface evolution using parametric maximum flows. Int. J. Comput. Vis. 84, 288–307 (2009) CrossRef
6.
Zurück zum Zitat Chambolle, A., Lions, P.-L.: Image recovery via total variation minimization and related problems. Numer. Math. 76(2), 167–188 (1997) MathSciNetMATHCrossRef Chambolle, A., Lions, P.-L.: Image recovery via total variation minimization and related problems. Numer. Math. 76(2), 167–188 (1997) MathSciNetMATHCrossRef
7.
Zurück zum Zitat Chan, T.F., Golub, G.H., Mulet, P.: A nonlinear primal-dual method for total variation-based image restoration. SIAM J. Sci. Comput. 20(6), 1964–1977 (1999) MathSciNetMATHCrossRef Chan, T.F., Golub, G.H., Mulet, P.: A nonlinear primal-dual method for total variation-based image restoration. SIAM J. Sci. Comput. 20(6), 1964–1977 (1999) MathSciNetMATHCrossRef
8.
Zurück zum Zitat Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward-backward splitting. Multiscale Model. Simul. 4(4), 1168–1200 (2005) MathSciNetMATHCrossRef Combettes, P.L., Wajs, V.R.: Signal recovery by proximal forward-backward splitting. Multiscale Model. Simul. 4(4), 1168–1200 (2005) MathSciNetMATHCrossRef
9.
Zurück zum Zitat Daubechies, I., Defrise, M., DeMol, C.: An iterative thresholding algorithm for linear inverse problems. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004) MathSciNetMATHCrossRef Daubechies, I., Defrise, M., DeMol, C.: An iterative thresholding algorithm for linear inverse problems. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004) MathSciNetMATHCrossRef
10.
Zurück zum Zitat Daubechies, I., Teschke, G., Vese, L.: Iteratively solving linear inverse problems under general convex constraints. Inverse Probl. Theor. Imaging 1(1), 29–46 (2007) MathSciNetMATHCrossRef Daubechies, I., Teschke, G., Vese, L.: Iteratively solving linear inverse problems under general convex constraints. Inverse Probl. Theor. Imaging 1(1), 29–46 (2007) MathSciNetMATHCrossRef
11.
Zurück zum Zitat Darbon, J., Sigelle, M.: A fast and exact algorithm for total variation minimization. In: IbPRIA 2005. Lecture Notes in Computer Science, vol. 3522, pp. 717–765 (2005) Darbon, J., Sigelle, M.: A fast and exact algorithm for total variation minimization. In: IbPRIA 2005. Lecture Notes in Computer Science, vol. 3522, pp. 717–765 (2005)
12.
Zurück zum Zitat Dobson, D., Vogel, C.R.: Convergence of an iterative method for total variation denoising. SIAM J. Numer. Anal. 34(5), 1779–1791 (1997) MathSciNetMATHCrossRef Dobson, D., Vogel, C.R.: Convergence of an iterative method for total variation denoising. SIAM J. Numer. Anal. 34(5), 1779–1791 (1997) MathSciNetMATHCrossRef
13.
Zurück zum Zitat Engl, HW., Hanke, M., Neubauer, A.: Regularization of inverse problems. In: Mathematics and Its Applications (Dordrecht), vol. 375. Kluwer Academic Publishers, Dordrecht (1996) Engl, HW., Hanke, M., Neubauer, A.: Regularization of inverse problems. In: Mathematics and Its Applications (Dordrecht), vol. 375. Kluwer Academic Publishers, Dordrecht (1996)
14.
Zurück zum Zitat Evans, L.C., Gariepy, R.F.: Measure Theory and Fine Properties of Functions. CRC Press, Boca Raton (1992) MATH Evans, L.C., Gariepy, R.F.: Measure Theory and Fine Properties of Functions. CRC Press, Boca Raton (1992) MATH
15.
Zurück zum Zitat Fornasier, M.: Domain decomposition methods for linear inverse problems with sparsity constraints. Inverse Probl. 23(6), 2505–2526 (2007) MathSciNetMATHCrossRef Fornasier, M.: Domain decomposition methods for linear inverse problems with sparsity constraints. Inverse Probl. 23(6), 2505–2526 (2007) MathSciNetMATHCrossRef
16.
Zurück zum Zitat Fornasier, M., Langer, A., Schönlieb, C.-B.: A convergent overlapping domain decomposition method for total variation minimization. Numer. Math. 116(4), 645–685 (2010) MathSciNetMATHCrossRef Fornasier, M., Langer, A., Schönlieb, C.-B.: A convergent overlapping domain decomposition method for total variation minimization. Numer. Math. 116(4), 645–685 (2010) MathSciNetMATHCrossRef
17.
Zurück zum Zitat Fornasier, M., Schönlieb, C.-B.: Subspace correction methods for total variation and ℓ 1-minimization. SIAM J. Numer. Anal. 47(4), 3397–3428 (2009) MathSciNetMATHCrossRef Fornasier, M., Schönlieb, C.-B.: Subspace correction methods for total variation and 1-minimization. SIAM J. Numer. Anal. 47(4), 3397–3428 (2009) MathSciNetMATHCrossRef
18.
Zurück zum Zitat Frick, K., Scherzer, O.: Regularization of ill-posed linear equations by the non-stationary augmented Lagrangian method. J. Integral Equ. Appl. 22(2), 217–257 (2010) MathSciNetMATHCrossRef Frick, K., Scherzer, O.: Regularization of ill-posed linear equations by the non-stationary augmented Lagrangian method. J. Integral Equ. Appl. 22(2), 217–257 (2010) MathSciNetMATHCrossRef
19.
Zurück zum Zitat Goldstein, T., Osher, S.: The split Bregman method for ℓ 1 regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009) MathSciNetMATHCrossRef Goldstein, T., Osher, S.: The split Bregman method for 1 regularized problems. SIAM J. Imaging Sci. 2(2), 323–343 (2009) MathSciNetMATHCrossRef
20.
Zurück zum Zitat Hintermüller, M., Stadler, G.: An infeasible primal-dual algorithm for total bounded variation-based inf-convolution-type image restoration. SIAM J. Sci. Comput. 28(1), 1–23 (2006) MathSciNetMATHCrossRef Hintermüller, M., Stadler, G.: An infeasible primal-dual algorithm for total bounded variation-based inf-convolution-type image restoration. SIAM J. Sci. Comput. 28(1), 1–23 (2006) MathSciNetMATHCrossRef
21.
Zurück zum Zitat Ito, K., Kunisch, K.: Lagrange Multiplier Approach to Variational Problems and Applications. Advances in Design and Control, vol. 15. SIAM, Philadelphia (2008) MATHCrossRef Ito, K., Kunisch, K.: Lagrange Multiplier Approach to Variational Problems and Applications. Advances in Design and Control, vol. 15. SIAM, Philadelphia (2008) MATHCrossRef
22.
Zurück zum Zitat Langer, A.: Convergence analysis of a non-overlapping domain decomposition algorithm for total variation minimization. J. Comput. Appl. Math. (2009, submitted) Langer, A.: Convergence analysis of a non-overlapping domain decomposition algorithm for total variation minimization. J. Comput. Appl. Math. (2009, submitted)
23.
Zurück zum Zitat Langer, A.: Subspace correction and domain decomposition methods for total variation minimization. PhD Thesis, Johannes Kepler University Linz (2011) Langer, A.: Subspace correction and domain decomposition methods for total variation minimization. PhD Thesis, Johannes Kepler University Linz (2011)
24.
Zurück zum Zitat Loris, I.: On the performance of algorithms for the minimization of 1-penalized functionals. Inverse Probl. 25, 035007 (2009) MathSciNetCrossRef Loris, I.: On the performance of algorithms for the minimization of 1-penalized functionals. Inverse Probl. 25, 035007 (2009) MathSciNetCrossRef
26.
Zurück zum Zitat Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460–489 (2005) MathSciNetMATHCrossRef Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460–489 (2005) MathSciNetMATHCrossRef
27.
Zurück zum Zitat Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992) MATHCrossRef Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992) MATHCrossRef
28.
Zurück zum Zitat Schönlieb, C.-B.: Total variation minimization with an H −1 constraint. In: Singularities in Nonlinear Evolution Phenomena and Applications Proceedings, Scuola Normale Superiore Pisa. CRM Series, vol. 9, pp. 201–232 (2009) Schönlieb, C.-B.: Total variation minimization with an H −1 constraint. In: Singularities in Nonlinear Evolution Phenomena and Applications Proceedings, Scuola Normale Superiore Pisa. CRM Series, vol. 9, pp. 201–232 (2009)
29.
Zurück zum Zitat Setzer, S.: Split Bregman algorithm, Douglas–Rachford splitting and frame shrinkage. In: Tai, X.-C., et al. (eds.) Proceedings of the Second International Conference on Scale Space Methods and Proceedings of the 2nd International Conference on Scale Space and Variational Methods in Computer Vision. LNCS, vol. 5567, pp. 464–476. Springer, Berlin (2009) CrossRef Setzer, S.: Split Bregman algorithm, Douglas–Rachford splitting and frame shrinkage. In: Tai, X.-C., et al. (eds.) Proceedings of the Second International Conference on Scale Space Methods and Proceedings of the 2nd International Conference on Scale Space and Variational Methods in Computer Vision. LNCS, vol. 5567, pp. 464–476. Springer, Berlin (2009) CrossRef
30.
31.
Zurück zum Zitat Yin, W., Osher, S., Goldfarb, D., Darbon, J.: Bregman iterative algorithms for ℓ 1-minimization with applications to compressed sensing. SIAM J. Imaging Sci. 1(1), 143–168 (2008) MathSciNetMATHCrossRef Yin, W., Osher, S., Goldfarb, D., Darbon, J.: Bregman iterative algorithms for 1-minimization with applications to compressed sensing. SIAM J. Imaging Sci. 1(1), 143–168 (2008) MathSciNetMATHCrossRef
32.
Zurück zum Zitat Zhang, X., Burger, M., Bresson, X., Osher, S.: Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Imaging Sci. 3(3), 253–276 (2010) MathSciNetMATHCrossRef Zhang, X., Burger, M., Bresson, X., Osher, S.: Bregmanized nonlocal regularization for deconvolution and sparse reconstruction. SIAM J. Imaging Sci. 3(3), 253–276 (2010) MathSciNetMATHCrossRef
Metadaten
Titel
Bregmanized Domain Decomposition for Image Restoration
verfasst von
Andreas Langer
Stanley Osher
Carola-Bibiane Schönlieb
Publikationsdatum
01.02.2013
Verlag
Springer US
Erschienen in
Journal of Scientific Computing / Ausgabe 2-3/2013
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI
https://doi.org/10.1007/s10915-012-9603-x

Weitere Artikel der Ausgabe 2-3/2013

Journal of Scientific Computing 2-3/2013 Zur Ausgabe

Preface

Preface