Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 4, Number 1, January 2010
Page(s) 33 - 38
DOI https://doi.org/10.1051/ijsmdo/2010005
Published online 21 July 2011
  1. H. Andrews, B. Hunt. Digital image restoration, Prentice-Hall, Engelwood Cliffs, NJ, (1977).
  2. A.K. Jain. Fundamentals of digital image processing, Prentice-Hall, Engelwood Cliffs, NJ, (1989).
  3. R.L. Lagendjik, J. Biemond. Iterative identification and restoration of images, Norwell, MA: Kluwer Academic Publishers, (1991).
  4. H.W. Engl, M. Hanke, A. Neubauer. Regularization of inverse problems, Kluwer, Dordrecht, The Netherlands, 71-143, (1996).
  5. J. Kamm, J.G. Nagy. Kronecker product and SVD approximations in image restoration, linear Algebra and its Applications, 284, 177-192, (1998).
  6. J. Kamm, J.G. Nagy. Kronecker product approximation for restoration image with reflexive boundary conditions, SIAM J. Matrix Anal. Appl., 25 (3), 829-841, (2004).
  7. C.F. Van Loan, N.P. Pitsianis. Approximation with Kronecker products, M.S. Moonen, G.H. Golub (Eds.), Linear Algebra for large scale and real time applications, Kluwer Academic Publishers, Dordrecht, 293-314, (1993).
  8. G.R. Ayers, J.C. Dainty. Iterative blind deconvolution method and its applications, Optics Letters 13 (7), 547-549, (1988).
  9. L.B. Lucy,. An iterative technique for the rectification of observed distributions, Astronomical Journal, 79, 745-754, (1974). [NASA ADS] [CrossRef]
  10. W.H. Richardson. Bayeian-based iterative method of image restoration, J. Optic. Soc. Amer. A, 62, 55-59, (1972). [NASA ADS] [CrossRef]
  11. A. Pruessner, D.P. O'Leary. Blind deconvolution using a regularized structured total least norm algorithm, SIAM J. Matric Anal. Appl. 24 (4), 1018-1037, (2003).
  12. G.H. Golub, U. Von Matt. Tikhonov regularization for large scale problems, G.H. Golub, S.H. Lui, F. Luk, R. Plemmons (Eds.), Workshop on Scientific Computing, Springer, New York, 3-26, (1997).
  13. A. Bouhamidi, K. Jbilou. Sylvester Tokhonov regularization methods in image restoration, J. Comput. Appl. Math. 206 (1), 86-98, (2007).
  14. M. Hanke, P.C. Hansen. Regularization methods for large-scale problems, Surveys Math. Indust., 3, 253-315, (1993). [MathSciNet]
  15. P.C. Hansen. Analysis of discrete ill-posed problems by means of the L-curve, SIAM Rev., 34, 561-580, (1992). [CrossRef] [MathSciNet]
  16. D. Calvetti, G.H. Golub, L. Reichel. Estimation of the L-curve via lanczos bidiagonalization, BIT, 39, 603-619, (1999). [CrossRef] [MathSciNet]
  17. D. Calvetti, B. Lewis, L. Reichel. GMRES, L-curves and discrete ill-posed problems, BIT, 42, 44-65, (2002). [CrossRef] [MathSciNet]
  18. G.H. Golub, M. Heath, G. Wahba. Generalized cross-validation as a method for choosing a good ridge parameter, Technometrics, 21, 215-223, (1979). [CrossRef] [MathSciNet]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.