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lgli/Foundations of Computational Imaging - A Model-Based Approach - Charles A. Bouman - SIAM - 2022.pdf
Foundations of computational imaging : a model-based approach 🔍
Charles Addison Bouman Society for Industrial and Applied Mathematics; SIAM, Other Titles in Applied Mathematics, 1, 2022
英语 [en] · PDF · 25.1MB · 2022 · 📘 非小说类图书 · 🚀/lgli/lgrs/zlib · Save
描述
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.
备用文件名
lgrsnf/Foundations of Computational Imaging - A Model-Based Approach - Charles A. Bouman - SIAM - 2022.pdf
备用文件名
zlib/no-category/Charles A. Bouman/Foundations of Computational Imaging: A Model-Based Approach_115179972.pdf
备选标题
A Model -based Approach
备用出版商
SIAM, Society for Industrial and Applied Mathematics
备用出版商
University of Maryland, Baltimore, OEA
备用版本
Other Titles in Applied Mathematics, Philadelphia, PA, 2022
备用版本
United States, United States of America
备用版本
OT, 180, Philadelphia, 2022
备用描述
Collecting a set of classical and emerging methods previously unavailable in a single resource, 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 brings together a blend of research with applications in a variety of 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. Readers will find basic techniques of model-based image processing, a comprehensive treatment of Bayesian and regularized image reconstruction methods, and an integrated treatment of advanced reconstruction techniques, such as majorization, constrained optimization, alternating direction method of multipliers (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, data science, numerical optimization, and approximation theory. It will also prove useful to researchers or practitioners in medical, scientific, commercial, and industrial imaging.
备用描述
Probability, estimation, and random processes -- Causal Gaussian models -- Non-causal Gaussian models -- Map estimation with Gaussian priors -- Non-Gaussian MRF models -- Map estimation with non-Gaussian priors -- Surrogate functions and majorization -- Constrained optimization and proximal methods -- Plug-and-play and advanced priors -- Model parameter estimation -- The expectation-maximization (EM) algorithm -- Markov chains and hidden Markov models -- General MRF models -- Stochastic simulation -- Bayesian segmentation -- Poisson data models.
备用描述
When I first started writing this book 20 years ago, Computational Imaging did not yet exist as a field. But at the time, I had a growing sense that the next generation of imaging systems required the integration of algorithms with sensors and that a suite of common analytical and computational tools was emerging for solving these problems and designing these systems
备用描述
"This book provides a foundation for a collection of theoretical material that can serve as a common language for both researchers and practitioners of Computational Imaging"-- Provided by publisher
开源日期
2024-10-18
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