Sparse Image and Signal Processing (Hardcover)

Jean-Luc Starck

ISBN 10: 1107088062 / ISBN 13: 9781107088061

Published by Cambridge University Press, Cambridge, 2015

About this Item

Hardcover. This thoroughly updated new edition presents state-of-the-art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB and IDL code, available online at , accompany these methods and all applications. This thoroughly updated edition presents state-of-the-art sparse and multiscale image and signal processing with applications in astronomy, biology, physics, MRI, digital media, and forensics. New chapters and sections cover dictionary learning, 3-D data (data cubes), and geo-located data. MATLAB and IDL code are available. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Seller Inventory # 9781107088061

Bibliographic Details

• Title: Sparse Image and Signal Processing (Hardcover)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2015
• Language: English
• ISBN 10: 1107088062
• ISBN 13: 9781107088061
• Binding: Hardcover
• Edition: 2nd Edition
• Condition: new

About this title

Synopsis

This thoroughly updated new edition presents state of the art sparse and multiscale image and signal processing. It covers linear multiscale geometric transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and mathematical morphology operators. Along with an up-to-the-minute description of required computation, it covers the latest results in inverse problem solving and regularization, sparse signal decomposition, blind source separation, in-painting, and compressed sensing. New chapters and sections cover multiscale geometric transforms for three-dimensional data (data cubes), data on the sphere (geo-located data), dictionary learning, and nonnegative matrix factorization. The authors wed theory and practice in examining applications in areas such as astronomy, including recent results from the European Space Agency's Herschel mission, biology, fusion physics, cold dark matter simulation, medical MRI, digital media, and forensics. MATLAB® and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.

About the Authors

Jean-Luc Starck is Senior Scientist at the Institute of Research into the Fundamental Laws of the Universe, Commissariat à l'énergie atomique, Saclay, France. He was awarded the 2022 Tycho Brahe Medal by the European Astronomical Society. His research interests include cosmology, weak lensing data, and statistical methods such as wavelets and other sparse representations of data. He has published over 200 papers in astrophysics, cosmology, signal processing, and applied mathematics, and is also author of three books.

Fionn Murtagh has served in the Space Science Department of the European Space Agency for twelve years. He is a Fellow of both the International Association for Pattern Recognition and the British Computer Society, as well as an elected member of the Royal Irish Academy and of Academia Europaea. He is a member of the editorial boards of many journals, and has been editor-in-chief of the Computer Journal for more than ten years.

Jalal M. Fadili has been full professor at Institut Universitaire de France since October 2013. His research interests include signal and image processing, statistics, optimization theory, and low-complexity regularization. He is a member of the editorial boards of several journals.