Variational Regularization for Systems of Inverse Problems

Tikhonov Regularization with Multiple Forward Operators

  • Richard Huber

Part of the BestMasters book series (BEST)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Richard Huber
    Pages 1-13
  3. Richard Huber
    Pages 15-37
  4. Richard Huber
    Pages 39-61
  5. Richard Huber
    Pages 63-88
  6. Back Matter
    Pages 129-136

About this book


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.


  • General Tikhonov Regularization
  • Specific Discrepancies
  • Regularization Functionals
  • Application to STEM Tomography Reconstruction

Target Groups

  • Researchers and students in the field of mathematics
  • Experts in the areas of mathematics, imaging, computer vision and nanotechnology

The Author
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.


Applied Mathematics Mathematical Image Processing Radon Transform Tomography Reconstruction Total Generalized Variation Electron Tomography Kullback-Leibler Discrepancy Ill-Posed Inverse Problems

Authors and affiliations

  • Richard Huber
    • 1
  1. 1.GrazAustria

Bibliographic information

  • DOI
  • Copyright Information Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2019
  • Publisher Name Springer Spektrum, Wiesbaden
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-658-25389-9
  • Online ISBN 978-3-658-25390-5
  • Series Print ISSN 2625-3577
  • Series Online ISSN 2625-3615
  • Buy this book on publisher's site