Rank-Deficient and Discrete Ill-Posed Problems

Rank-Deficient and Discrete Ill-Posed Problems

Author: Per Christian Hansen

Publisher: SIAM

Published: 2005-01-01

Total Pages: 259

ISBN-13: 0898714036

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Here is an overview of modern computational stabilization methods for linear inversion, with applications to a variety of problems in audio processing, medical imaging, tomography, seismology, astronomy, and other areas. Rank-deficient problems involve matrices that are either exactly or nearly rank deficient. Such problems often arise in connection with noise suppression and other problems where the goal is to suppress unwanted disturbances of the given measurements. Discrete ill-posed problems arise in connection with the numerical treatment of inverse problems, where one typically wants to compute information about some interior properties using exterior measurements. Examples of inverse problems are image restoration and tomography, where one needs to improve blurred images or reconstruct pictures from raw data. This book describes, in a common framework, new and existing numerical methods for the analysis and solution of rank-deficient and discrete ill-posed problems. The emphasis is on insight into the stabilizing properties of the algorithms and on the efficiency and reliability of the computations. The setting is that of numerical linear algebra rather than abstract functional analysis, and the theoretical development is complemented with numerical examples and figures that illustrate the features of the various algorithms.


A Taste of Inverse Problems

A Taste of Inverse Problems

Author: Martin Hanke

Publisher: SIAM

Published: 2017-01-01

Total Pages: 171

ISBN-13: 1611974933

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Inverse problems need to be solved in order to properly interpret indirect measurements. Often, inverse problems are ill-posed and sensitive to data errors. Therefore one has to incorporate some sort of regularization to reconstruct significant information from the given data. A Taste of Inverse Problems: Basic Theory and Examples?presents the main achievements that have emerged in regularization theory over the past 50 years, focusing on linear ill-posed problems and the development of methods that can be applied to them. Some of this material has previously appeared only in journal articles. This book rigorously discusses state-of-the-art inverse problems theory, focusing on numerically relevant aspects and omitting subordinate generalizations; presents diverse real-world applications, important test cases, and possible pitfalls; and treats these applications with the same rigor and depth as the theory.


Handbook of Mathematical Methods in Imaging

Handbook of Mathematical Methods in Imaging

Author: Otmar Scherzer

Publisher: Springer Science & Business Media

Published: 2010-11-23

Total Pages: 1626

ISBN-13: 0387929193

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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. 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 150 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.


Parameter Estimation and Inverse Problems

Parameter Estimation and Inverse Problems

Author: Richard C. Aster

Publisher: Elsevier

Published: 2018-10-16

Total Pages: 406

ISBN-13: 0128134232

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Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more. - Features examples that are illustrated with simple, easy to follow problems that illuminate the details of a particular numerical method - Includes an online instructor's guide that helps professors teach and customize exercises and select homework problems - Covers updated information on adjoint methods that are presented in an accessible manner


Deblurring Images

Deblurring Images

Author: Per Christian Hansen

Publisher: SIAM

Published: 2006-01-01

Total Pages: 144

ISBN-13: 9780898718874

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Describes the deblurring algorithms and techniques collectively known as spectral filtering methods, in which the singular value decomposition, or a similar decomposition with spectral properties, is used to introduce the necessary regularization or filtering in the reconstructed image. The concise MATLAB® implementations described in the book provide a template of techniques that can be used to restore blurred images from many applications.


Exact and Approximate Modeling of Linear Systems

Exact and Approximate Modeling of Linear Systems

Author: Ivan Markovsky

Publisher: SIAM

Published: 2006-01-31

Total Pages: 210

ISBN-13: 0898716039

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Exact and Approximate Modeling of Linear Systems: A Behavioral Approach elegantly introduces the behavioral approach to mathematical modeling, an approach that requires models to be viewed as sets of possible outcomes rather than to be a priori bound to particular representations. The authors discuss exact and approximate fitting of data by linear, bilinear, and quadratic static models and linear dynamic models, a formulation that enables readers to select the most suitable representation for a particular purpose. This book presents exact subspace-type and approximate optimization-based identification methods, as well as representation-free problem formulations, an overview of solution approaches, and software implementation. Readers will find an exposition of a wide variety of modeling problems starting from observed data. The presented theory leads to algorithms that are implemented in C language and in MATLAB.


Computed Tomography

Computed Tomography

Author: Per Christian Hansen

Publisher: SIAM

Published: 2021-09-25

Total Pages: 355

ISBN-13: 1611976677

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This book describes fundamental computational methods for image reconstruction in computed tomography (CT) with a focus on a pedagogical presentation of these methods and their underlying concepts. Insights into the advantages, limitations, and theoretical and computational aspects of the methods are included, giving a balanced presentation that allows readers to understand and implement CT reconstruction algorithms. Unique in its emphasis on the interplay between modeling, computing, and algorithm development, Computed Tomography: Algorithms, Insight, and Just Enough Theory develops the mathematical and computational aspects of three main classes of reconstruction methods: classical filtered back-projection, algebraic iterative methods, and variational methods based on nonlinear numerical optimization algorithms. It spotlights the link between CT and numerical methods, which is rarely discussed in current literature, and describes the effects of incomplete data using both microlocal analysis and singular value decomposition (SVD). This book sets the stage for further exploration of CT algorithms. Readers will be able to grasp the underlying mathematical models to motivate and derive the basic principles of CT reconstruction and will gain basic understanding of fundamental computational challenges of CT, such as the influence of noisy and incomplete data, as well as the reconstruction capabilities and the convergence of the iterative algorithms. Exercises using MATLAB are included, allowing readers to experiment with the algorithms and making the book suitable for teaching and self-study. Computed Tomography: Algorithms, Insight, and Just Enough Theory is primarily aimed at students, researchers, and practitioners interested in the computational aspects of X-ray CT and is also relevant for anyone working with other forms of tomography, such as neutron and electron tomography, that share the same mathematical formulation. With its basis in lecture notes developed for a PhD course, it is appropriate as a textbook for courses on computational methods for X-ray CT and computational methods for inverse problems.


Lunar Gravimetry

Lunar Gravimetry

Author: Rune Floberghagen

Publisher: Springer Science & Business Media

Published: 2012-08-27

Total Pages: 296

ISBN-13: 9048195527

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Lunar Gravimetry: Revealing the Far-Side provides a thorough and detailed discussion of lunar gravity field research and applications, from the initial efforts of the pre-Apollo and Luna eras to the dedicated gravity mapping experiments of the third millennium. Analysis of the spatial variations of the gravity field of the Moon is a key selenodetic element in the understanding of the physics of the Moon's interior. Remarkably, more than forty years after the initial steps in lunar exploration by spacecraft, the global gravity field still remains largely unknown, due to the limitations of standard observations techniques. As such, knowledge of the high-accuracy and high-resolution gravity field is one of the remaining unsolved issues in lunar science.


Discrete Inverse Problems

Discrete Inverse Problems

Author: Per Christian Hansen

Publisher: SIAM

Published: 2010-01-01

Total Pages: 220

ISBN-13: 089871883X

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This book gives an introduction to the practical treatment of inverse problems by means of numerical methods, with a focus on basic mathematical and computational aspects. To solve inverse problems, we demonstrate that insight about them goes hand in hand with algorithms.