Skip to main content
Log in

A feasible direction method for image restoration

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

In this work, a feasible direction method is proposed for computing the regularized solution of image restoration problems by simply using an estimate of the noise present on the data. The problem is formulated as an optimization problem with one quadratic constraint. The proposed method computes a feasible search direction by inexactly solving a trust region subproblem with the truncated Conjugate Gradient method of Steihaug. The trust region radius is adjusted to maintain feasibility and a line-search globalization strategy is employed. The global convergence of the method is proved. The results of image denoising and deblurring are presented in order to illustrate the effectiveness and efficiency of the proposed method.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bertero M., Boccacci P.: Introduction to Inverse Problems in Imaging. IOP Publishing, Bristol (1998)

    Book  MATH  Google Scholar 

  2. Bertsekas D.: Nonlinear Programming, 2nd edn. Athena Scientific, Belmont (1999)

    MATH  Google Scholar 

  3. Conn, A.R., Gould, N.I.M., Toint, Ph.L.: Trust-region methods. In: MPS/SIAM Series on Optimization. SIAM, Philadelphia (2000)

  4. Csiszár I.: Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. Ann. Statist. 19, 2032–2066 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  5. Dobson D., Santosa F.: Recovery of blocky images from noisy and blurred data. SIAM J. Appl. Math. 56, 1181–1198 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  6. Fortin C., Wolkowicz H.: The trust-region subproblem and semidefinite programming. Optim. Methods Softw. 19(1), 41–67 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  7. Hansen P.C.: Rank-Deficient and Discrete Ill-Posed Problems. SIAM, Philadelphia (1998)

    Book  Google Scholar 

  8. Hansen P.C., Nagy J., O’Leary D.P.: Deblurring Images. Matrices, Spectra and Filtering. SIAM, Philadelphia (2006)

    Book  MATH  Google Scholar 

  9. Heinkenschloss M.: A trust region method for norm constrained problems. SIAM J. Numer. Anal. 35, 1594–1620 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  10. Martínez J.M., Santos S.A.: A trust-region strategy for minimization on arbitrary domains. Math. Prog. 68, 267–301 (1995)

    Article  MATH  Google Scholar 

  11. Morè J.J.: Recent developments in algorithms and software for trust region methods. In: Bachem, A., Grötschel, M., Korte, B. (eds) Mathematical Programming: The State of the Art, pp. 258–287. Springer, Berlin (1983)

    Chapter  Google Scholar 

  12. Nocedal J., Wright S.J.: Numerical Optimization. Springer-Verlag New York, Inc., New York (1999)

    Book  MATH  Google Scholar 

  13. Noll D.: Restoration of degraded images with maximum entropy. J. Global Optim. 10, 91–103 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  14. Ortega J.M., Rheinboldt W.C.: Iterative solution of nonlinear equations in several variables. In: Classics in Applied Mathematics. SIAM, Philadelphia (2000)

    Book  Google Scholar 

  15. Rendl F., Wolkowicz H.: A semidefinite framework for trust region subproblems with applications to large scale minimization. Math. Prog. 77(2), 273–299 (1997)

    MathSciNet  MATH  Google Scholar 

  16. Rojas M., Santos S.A., Sorensen D.C.: A new matrix-free algorithm for the large-scale trust-region subproblem. SIAM J. Optim. 11(3), 611–646 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  17. Rojas M., Santos S.A., Sorensen D.C.: Algorithm 873: LSTRS: MATLAB software for large-scale trust-region subproblems and regularization. ACM Trans. Math. Softw. 34(2), 11 (2008)

    Article  MathSciNet  Google Scholar 

  18. Rudin L.I., Osher S., Fatemi E.: Nonlinear total variation based noise removal algorithms. Phys. D 60, 259–268 (1992)

    Article  MATH  Google Scholar 

  19. Ruggiero V., Swerafini T., Zanella R., Zanni L.: Iterative regularization algorithms for constrained image deblurring on graphics processors. J. Global Optim. 48, 145–157 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  20. Saad Y., Schultz M.H.: GMRES: A generalized minimal residual algorithm for solving nonsymmetric systems. SIAM J. Sci. Statist. Comput. 7, 856–869 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  21. Steihaug T.: The conjugate gradient method and trust regions in large scale optimization. SIAM J. Numer. Anal. 20(3), 626–637 (1983)

    Article  MathSciNet  MATH  Google Scholar 

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

    MATH  Google Scholar 

  23. Vogel C.R.: Computational Methods for Inverse Problems. SIAM, Philadelphia (2002)

    Book  MATH  Google Scholar 

  24. Vogel C.R., Oman M.E.: Iterative methods for total variation denoising. SIAM J. Sci. Comput. 17, 227–238 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  25. Vogel C.R., Oman M.E.: Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Trans. Image Proc. 7, 813–824 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  26. Zangwill W.I.: Nonlinear Programming: A Unified Approach. Prentice-Hall, Englewood Cliffs (1969)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. Loli Piccolomini.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Landi, G., Piccolomini, E.L. A feasible direction method for image restoration. Optim Lett 6, 1795–1817 (2012). https://doi.org/10.1007/s11590-011-0378-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-011-0378-z

Keywords

Navigation