Variational Regularization for Systems of Inverse Problems

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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.ContentsGeneral Tikhonov RegularizationSpecific DiscrepanciesRegularization FunctionalsApplication to STEM Tomography Reconstruction Target Groups Researchers and students in the field of mathematicsExperts in the areas of mathematics, imaging, computer vision and nanotechnology The AuthorRichard 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.