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...

Ausführliche Beschreibung

Bibliographische Detailangaben

1. Verfasser:	Bouman, Charles Addison (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Philadelphia SIAM, Society for Industrial and Applied Mathematics [2022]
Schriftenreihe:	Other Titles in Applied Mathematics [180]
Schlagworte:	Maschinelles Sehen Bildverarbeitung Image processing / Digital techniques / Mathematics
Zusammenfassung:	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. 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.
Beschreibung:	Includes bibliographical references and index
Beschreibung:	xi, 337 Seiten Illustrationen, Diagramme
ISBN:	9781611977134