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.

A general framework for nonlinear regularized Krylov-based image restoration

MORIGI, SERENA;REICHEL, LOTHAR;SGALLARI, FIORELLA
2014

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.
2014
Lecture Notes in Computer Science LNCS 8641
273
279
S. Morigi; L. Reichel; F. Sgallari
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/407773
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