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Computational Methods for Inverse Problems

Computational Methods for Inverse Problems

£57.00

Part of Frontiers in Applied Mathematics

  • Date Published: May 2007
  • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • format: Paperback
  • isbn: 9780898715507

£ 57.00
Paperback

This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
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  • Inverse problems arise in a number of important practical applications, ranging from biomedical imaging to seismic prospecting. This book provides the reader with a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems. It also addresses specialized topics like image reconstruction, parameter identification, total variation methods, nonnegativity constraints, and regularization parameter selection methods. Because inverse problems typically involve the estimation of certain quantities based on indirect measurements, the estimation process is often ill-posed. Regularization methods, which have been developed to deal with this ill-posedness, are carefully explained in the early chapters of Computational Methods for Inverse Problems. The book also integrates mathematical and statistical theory with applications and practical computational methods, including topics like maximum likelihood estimation and Bayesian estimation.

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    Product details

    • Date Published: May 2007
    • format: Paperback
    • isbn: 9780898715507
    • length: 199 pages
    • dimensions: 254 x 178 x 10 mm
    • weight: 0.365kg
    • availability: This item is not supplied by Cambridge University Press in your region. Please contact Soc for Industrial & Applied Mathematics for availability.
  • Table of Contents

    Preface
    1. Introduction
    2. Analytical Tools
    3. Numerical Optimization Tools
    4. Statistical Estimation Theory
    5. Image Deblurring
    6. Parameter Identification
    7. Regularization Parameter Selection Methods
    8. Total Variation Regularization
    9. Nonnegativity Constraints
    Bibliography
    Index.

  • Author

    Curtis R. Vogel, Montana State University

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