Abstract
We use the two-dimensional DCT to study several properties of reconstructed images computed by regularizing iterations, that is, Krylov subspace methods applied to discrete ill-posed problems. The regularization in these methods is obtained via the projection onto the associated Krylov subspace. We focus on CGLS/LSQR, GMRES, and RRGMRES, as well as MINRES and MR-II in the symmetric case. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Hansen, P. C., & Jensen, T. K. (2007). Large-scale methods in image deblurring. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4699 LNCS, pp. 24–35). Springer Verlag. https://doi.org/10.1007/978-3-540-75755-9_3
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