Computational Methods for Inverse Problems
Seiten
1987
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-0-89871-550-7 (ISBN)
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-0-89871-550-7 (ISBN)
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.
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.
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.
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.
| Reihe/Serie | Frontiers in Applied Mathematics |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 365 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| ISBN-10 | 0-89871-550-4 / 0898715504 |
| ISBN-13 | 978-0-89871-550-7 / 9780898715507 |
| Zustand | Neuware |
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