Tikhonov regularization is a cornerstone technique in solving inverse problems with applications in countless scientific fields. Richard Huber discusses a multi-parameter Tikhonov approach for systems of inverse problems in order to take advantage of their specific structure. Such an approach allows to choose the regularization weights of each subproblem individually with respect to the corresponding noise levels and degrees of ill-posedness.
Table des matières
General Tikhonov Regularization.- Specific Discrepancies.- Regularization Functionals.- Application to STEM Tomography Reconstruction.
A propos de l’auteur
Richard Huber wrote his master’s thesis under the supervision of Prof. Dr. Kristian Bredies at the Institute for Mathematics and Scientific Computing at Graz University, Austria.
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