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.
Similar content being viewed by others
References
Bertero M., Boccacci P.: Introduction to Inverse Problems in Imaging. IOP Publishing, Bristol (1998)
Bertsekas D.: Nonlinear Programming, 2nd edn. Athena Scientific, Belmont (1999)
Conn, A.R., Gould, N.I.M., Toint, Ph.L.: Trust-region methods. In: MPS/SIAM Series on Optimization. SIAM, Philadelphia (2000)
Csiszár I.: Why least squares and maximum entropy? An axiomatic approach to inference for linear inverse problems. Ann. Statist. 19, 2032–2066 (1991)
Dobson D., Santosa F.: Recovery of blocky images from noisy and blurred data. SIAM J. Appl. Math. 56, 1181–1198 (1996)
Fortin C., Wolkowicz H.: The trust-region subproblem and semidefinite programming. Optim. Methods Softw. 19(1), 41–67 (2004)
Hansen P.C.: Rank-Deficient and Discrete Ill-Posed Problems. SIAM, Philadelphia (1998)
Hansen P.C., Nagy J., O’Leary D.P.: Deblurring Images. Matrices, Spectra and Filtering. SIAM, Philadelphia (2006)
Heinkenschloss M.: A trust region method for norm constrained problems. SIAM J. Numer. Anal. 35, 1594–1620 (1998)
Martínez J.M., Santos S.A.: A trust-region strategy for minimization on arbitrary domains. Math. Prog. 68, 267–301 (1995)
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)
Nocedal J., Wright S.J.: Numerical Optimization. Springer-Verlag New York, Inc., New York (1999)
Noll D.: Restoration of degraded images with maximum entropy. J. Global Optim. 10, 91–103 (1997)
Ortega J.M., Rheinboldt W.C.: Iterative solution of nonlinear equations in several variables. In: Classics in Applied Mathematics. SIAM, Philadelphia (2000)
Rendl F., Wolkowicz H.: A semidefinite framework for trust region subproblems with applications to large scale minimization. Math. Prog. 77(2), 273–299 (1997)
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)
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)
Rudin L.I., Osher S., Fatemi E.: Nonlinear total variation based noise removal algorithms. Phys. D 60, 259–268 (1992)
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)
Saad Y., Schultz M.H.: GMRES: A generalized minimal residual algorithm for solving nonsymmetric systems. SIAM J. Sci. Statist. Comput. 7, 856–869 (1986)
Steihaug T.: The conjugate gradient method and trust regions in large scale optimization. SIAM J. Numer. Anal. 20(3), 626–637 (1983)
Tikhonov A.N., Arsenin V.Y.: Solutions of Ill-Posed Problems. Wiley, New York (1977)
Vogel C.R.: Computational Methods for Inverse Problems. SIAM, Philadelphia (2002)
Vogel C.R., Oman M.E.: Iterative methods for total variation denoising. SIAM J. Sci. Comput. 17, 227–238 (1996)
Vogel C.R., Oman M.E.: Fast, robust total variation-based reconstruction of noisy, blurred images. IEEE Trans. Image Proc. 7, 813–824 (1998)
Zangwill W.I.: Nonlinear Programming: A Unified Approach. Prentice-Hall, Englewood Cliffs (1969)
Author information
Authors and Affiliations
Corresponding author
Rights 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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11590-011-0378-z