Foundations of Computational Imaging
A Model-Based Approach
Seiten
2022
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-712-7 (ISBN)
Society for Industrial & Applied Mathematics,U.S. (Verlag)
978-1-61197-712-7 (ISBN)
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Collecting a set of classical and emerging methods that otherwise would not be available in a single treatment, Foundations of Computational Imaging is the first book to define a common foundation for the mathematical and statistical methods used in computational imaging.
Collecting a set of classical and emerging methods that otherwise would not be available in a single treatment, Foundations of Computational Imaging: A Model-Based Approach is the first book to define a common foundation for the mathematical and statistical methods used in computational imaging. The book is designed to bring together an eclectic group of researchers with a wide variety of applications and disciplines including applied math, physics, chemistry, optics, and signal processing, to address a collection of problems that can benefit from a common set of methods. Inside, readers will find:
Basic techniques of model-based image processing.
A comprehensive treatment of Bayesian and regularized image reconstruction methods.
An integrated treatment of advanced reconstruction techniques such as majorization, constrained optimization, ADMM, and Plug-and-Play methods for model integration.
Foundations of Computational Imaging can be used in courses on Model-Based or Computational Imaging, Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory. It is also for researchers or practitioners in medical imaging, scientific imaging, commercial imaging, or industrial imaging.
Collecting a set of classical and emerging methods that otherwise would not be available in a single treatment, Foundations of Computational Imaging: A Model-Based Approach is the first book to define a common foundation for the mathematical and statistical methods used in computational imaging. The book is designed to bring together an eclectic group of researchers with a wide variety of applications and disciplines including applied math, physics, chemistry, optics, and signal processing, to address a collection of problems that can benefit from a common set of methods. Inside, readers will find:
Basic techniques of model-based image processing.
A comprehensive treatment of Bayesian and regularized image reconstruction methods.
An integrated treatment of advanced reconstruction techniques such as majorization, constrained optimization, ADMM, and Plug-and-Play methods for model integration.
Foundations of Computational Imaging can be used in courses on Model-Based or Computational Imaging, Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory. It is also for researchers or practitioners in medical imaging, scientific imaging, commercial imaging, or industrial imaging.
Charles A. Bouman is the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering at Purdue University. His research is in computational imaging, focusing on the integration of statistical signal processing, physics, and computation to solve problems with applications in healthcare, scientific, industrial, and consumer imaging. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tomography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products.
| Erscheinungsdatum | 17.08.2022 |
|---|---|
| Reihe/Serie | Other Titles in Applied Mathematics |
| Verlagsort | New York |
| Sprache | englisch |
| Gewicht | 630 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| ISBN-10 | 1-61197-712-6 / 1611977126 |
| ISBN-13 | 978-1-61197-712-7 / 9781611977127 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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