Regularization by fractional filter methods and data smoothing

Abstract

This paper is concerned with the regularization of linear ill-posed problems by a com- bination 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 �. 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 well- known drawback of oversmoothing. Convergence rates as well as numerical examples are presented.