Compressive Imaging: Structure, Sampling, Learning

Compressive Imaging: Structure, Sampling, Learning

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Résumé

This book provides a practical introduction to compressive imaging (with examples and code), an overview of core topics, and a comprehensive, rigorous treatment of the subject. It caters to graduate students, postdocs and faculty in mathematics, computer science, physics and engineering who want to learn about modern imaging techniques.

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Compressive Imaging: Structure, Sampling, Learning by Ben Adcock

Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging – including compressed sensing, wavelets and optimization – in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the pitfalls of these latest approaches.
Ben Adcock is Associate Professor of Mathematics at Simon Fraser University. He received the CAIMS/PIMS Early Career Award (2017), an Alfred P. Sloan Research Fellowship (2015) and a Leslie Fox Prize in Numerical Analysis (2011). He has published fifteen conference proceedings, two book chapters and over fifty peer-reviewed journal articles. His work has been published in outlets such as SIAM Review and Proceedings of the National Academy of Sciences, and featured on the cover of SIAM News. Anders C. Hansen is Reader in Mathematics at University of Cambridge and Professor of Mathematics at the University of Oslo. He received the Leverhulme Prize in Mathematics and Statistics (2017), the 2018 IMA Prize in Mathematics and Applications and the Whitehead Prize (2019). He has had papers published in outlets such as the Journal of the American Mathematical Society and Proceedings of the National Academy of Sciences, and featured on the cover of Physical Review Letters and SIAM News.
SKU Non disponible
ISBN 13 9781108421614
ISBN 10 110842161X
Titre Compressive Imaging: Structure, Sampling, Learning
Auteur Ben Adcock
État Non disponible
Type de reliure Hardback
Éditeur Cambridge University Press
Année de publication 2021-09-16
Nombre de pages 614
Note de couverture La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier.
Note Non disponible