Abstract
This paper introduces a new approach to computing an approximate solution of Tikhonov-regularized large-scale ill-posed problems with a general nonlinear regularization operator. The iterative method applies a sequence of projections onto generalized Krylov subspaces using a semi-implicit approach to deal with the nonlinearity in the regularization term. A suitable value of the regularization parameter is determined by the discrepancy principle. Computed examples illustrate the performance of the method applied to the restoration of blurred and noisy images.
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Morigi, S., Reichel, L., Sgallari, F. (2014). A General Framework for Nonlinear Regularized Krylov-Based Image Restoration. In: Zhang, Y.J., Tavares, J.M.R.S. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2014. Lecture Notes in Computer Science, vol 8641. Springer, Cham. https://doi.org/10.1007/978-3-319-09994-1_27
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DOI: https://doi.org/10.1007/978-3-319-09994-1_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09993-4
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