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- W4386566045 abstract "Iterated Tikhonov regularization methods are prevalent regularization methods that can make the solution of ill-posed problems less sensitive to noise. As the iteration number grows, their solution gradually converges to the optimum approximation of the desired solution but then changes to the worse, causing a semiconvergence. This difficulty can be reduced by a reliable early termination rule or a strategy for choosing the appropriate regularization parameter at each iteration. Different from the existing methods, this paper describes a novel iterative Tikhonov regularization based on introducing a non-decreasing sequence of relaxation parameters into the stationary iterated Tikhonov regularization, providing a fast and roughly stable convergence. After investigating theoretically the convergence rate of the novel iterative Tikhonov regularization, we propose a series of numerical experiments for the evaluation of its accuracy and finally describe a real-world application in the field of 3-dimensional image restoration problems to verify its efficiency." @default.
- W4386566045 created "2023-09-10" @default.
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- W4386566045 date "2024-02-01" @default.
- W4386566045 modified "2023-10-07" @default.
- W4386566045 title "A Relaxed Iterated Tikhonov Regularization for Linear Ill-posed Inverse Problems" @default.
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- W4386566045 doi "https://doi.org/10.1016/j.jmaa.2023.127754" @default.
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