Regularization of linear inverse problems with irregular noise using embedding operators
CoRR(2024)
摘要
In this paper, we investigate regularization of linear inverse problems with
irregular noise. In particular, we consider the case that the noise can be
preprocessed by certain adjoint embedding operators. By introducing the
consequent preprocessed problem, we provide convergence analysis for general
regularization schemes under standard assumptions. Furthermore, for a special
case of Tikhonov regularization in Computerized Tomography, we show that our
approach leads to a novel (Fourier-based) filtered backprojection algorithm.
Numerical examples with different parameter choice rules verify the efficiency
of our proposed algorithm.
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