Description
- 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.
About the Author
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.
Book Information
ISBN 9781611977127
Author Charles A. Bouman
Format Paperback
Page Count 337
Imprint Society for Industrial & Applied Mathematics,U.S.
Publisher Society for Industrial & Applied Mathematics,U.S.
Weight(grams) 630g