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.
Cuprins
General Tikhonov Regularization.- Specific Discrepancies.- Regularization Functionals.- Application to STEM Tomography Reconstruction.
Despre autor
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.
Limba Engleză ● Format PDF ● Pagini 136 ● ISBN 9783658253905 ● Mărime fișier 5.6 MB ● Editura Springer Fachmedien Wiesbaden ● Oraș Wiesbaden ● Țară DE ● Publicat 2019 ● Descărcabil 24 luni ● Valută EUR ● ID 6888611 ● Protecție împotriva copiilor DRM social