Table of contents

  1. Front Matter
    Pages i-xviii
  2. Inverse Problems – Methods

    1. Front Matter
      Pages 1-1
    2. Charles Groetsch
      Pages 3-46
    3. Julianne Chung, Sarah Knepper, James G. Nagy
      Pages 47-90
    4. Jin Cheng, Bernd Hofmann
      Pages 91-123
    5. Christiane Pöschl, Otmar Scherzer
      Pages 125-155
    6. Mila Nikolova
      Pages 157-204
    7. Massimo Fornasier, Holger Rauhut
      Pages 205-256
    8. Jonathan M. Borwein, D. Russell Luke
      Pages 257-304
    9. Charles Byrne, Paul P. B. Eggermont
      Pages 305-388
    10. Martin Burger, Barbara Kaltenbacher, Andreas Neubauer
      Pages 431-470
    11. Oliver Dorn, Dominique Lesselier
      Pages 471-532
  3. Inverse Problems – Case Examples

    1. Front Matter
      Pages 533-533
    2. Habib Ammari, Hyeonbae Kang
      Pages 535-590
    3. Martin Hanke-Bourgeois, Andreas Kirsch
      Pages 591-647
    4. David Colton, Rainer Kress
      Pages 649-700
    5. Andy Adler, Romina Gaburro, William Lionheart
      Pages 701-762
    6. Margaret Cheney, Brett Borden
      Pages 763-799
    7. Gabor T. Herman
      Pages 801-845

About this book


The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous.

This expanded and revised second edition contains updates to existing chapters and 16 additional entries on important mathematical methods such as graph cuts, morphology, discrete geometry, PDEs, conformal methods, to name a few. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 200 illustrations and an extended bibliography.

It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.


Algorithmic Reconstruction Mathematical Imaging and Vision Support Vector Machines Variation in Imaging Wave Phenomena

Editors and affiliations

  • Otmar Scherzer
    • 1
  1. 1.Computational Science CenterUniversity of ViennaViennaAustria

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media New York 2015
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4939-0789-2
  • Online ISBN 978-1-4939-0790-8
  • About this book