Computational Methods for Inverse Problems in Imaging

  • Marco Donatelli
  • Stefano Serra-Capizzano

Part of the Springer INdAM Series book series (SINDAMS, volume 36)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Silvia Bonettini, Federica Porta, Marco Prato, Simone Rebegoldi, Valeria Ruggiero, Luca Zanni
    Pages 1-31
  3. Davide Bianchi, Alessandro Buccini, Marco Donatelli
    Pages 33-49
  4. Pietro Dell’Acqua, Marco Donatelli, Lothar Reichel
    Pages 51-75
  5. Anna Maria Massone, Cristina Campi, Francesco Fiz, Mauro Carlo Beltrametti
    Pages 93-116
  6. Marco Prato, Andrea La Camera, Carmelo Arcidiacono, Patrizia Boccacci, Mario Bertero
    Pages 117-140
  7. Silvia Tozza, Maurizio Falcone
    Pages 141-166

About this book


This book presents recent mathematical methods in the area of inverse problems in imaging with a particular focus on the computational aspects and applications. The formulation of inverse problems in imaging requires accurate mathematical modeling in order to preserve the significant features of the image. The book describes computational methods to efficiently address these problems based on new optimization algorithms for smooth and nonsmooth convex minimization, on the use of structured (numerical) linear algebra, and on multilevel techniques. It also discusses various current and challenging applications in fields such as astronomy, microscopy, and biomedical imaging. The book is intended for researchers and advanced graduate students interested in inverse problems and imaging.


Image reconstruction Inverse problems Regularization Optimization methods Computational Methods

Editors and affiliations

  • Marco Donatelli
    • 1
  • Stefano Serra-Capizzano
    • 2
  1. 1.Department of Science and High TechnologyUniversity of InsubriaComoItaly
  2. 2.Department of Humanities and InnovationUniversity of InsubriaComoItaly

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-32881-8
  • Online ISBN 978-3-030-32882-5
  • Series Print ISSN 2281-518X
  • Series Online ISSN 2281-5198
  • Buy this book on publisher's site