Regularization by Fractional Filter Methods and Data Smoothing
نویسندگان
چکیده
This paper is concerned with the regularization of linear ill-posed problems by a combination of data smoothing and fractional filter methods. For the data smoothing, a wavelet shrinkage denoising is applied to the noisy data with known error level δ. For the reconstruction, an approximation to the solution of the operator equation is computed from the data estimate by fractional filter methods. These fractional methods are based on the classical Tikhonov and Landweber method but avoid at least partially the wellknown drawback of oversmoothing. Convergence rates as well as numerical examples are presented.
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