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Foundations of computational imaging : a model-based approach

Foundations of computational imaging : a model-based approach (1회 대출)

자료유형
단행본
개인저자
Bouman, Charles Addison, author.
서명 / 저자사항
Foundations of computational imaging : a model-based approach / Charles A. Bouman.
발행사항
Philadelphia :   Society for Industrial and Applied Mathematics,   2022.  
형태사항
xi, 337 p. : ill. ; 26 cm.
ISBN
9781611977127
요약
"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"--
내용주기
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.
서지주기
Includes bibliographical references and index.
일반주제명
Image processing --Digital techniques --Mathematics.
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100 1 ▼a Bouman, Charles Addison, ▼e author.
245 1 0 ▼a Foundations of computational imaging : ▼b a model-based approach / ▼c Charles A. Bouman.
260 ▼a Philadelphia : ▼b Society for Industrial and Applied Mathematics, ▼c 2022.
264 1 ▼a Philadelphia : ▼b Society for Industrial and Applied Mathematics, ▼c [2022]
300 ▼a xi, 337 p. : ▼b ill. ; ▼c 26 cm.
336 ▼a text ▼b txt ▼2 rdacontents
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
504 ▼a Includes bibliographical references and index.
505 0 ▼a 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.
520 ▼a "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"-- ▼c Provided by publisher.
650 0 ▼a Image processing ▼x Digital techniques ▼x Mathematics.
945 ▼a ITMT

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