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 perfor...

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Bibliographic Details

Main Authors:	Adcock, Ben ca. 20./21. Jh (Author), Hansen, Anders ca. 20./21. Jh (Author), Antun, Vegard ca. 20./21. Jh (Author)
Format:	Electronic eBook
Language:	English
Published:	Cambridge Cambridge University Press 2021
Subjects:	Image compression Digital images / Deconvolution Bildverarbeitung Datenkompression Entfaltung > Mathematik
Online Access:	BSB01 FHN01 Volltext
Summary:	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
Item Description:	Title from publisher's bibliographic system (viewed on 16 Jul 2021)
Physical Description:	1 Online-Ressource (xv, 601 Seiten)
ISBN:	9781108377447
DOI:	10.1017/9781108377447