Inbunden, Engelska, 2015

Sparse Image and Signal Processing

Wavelets and Related Geometric Multiscale Analysis, Second Edition

Av Jean-Luc Starck, Fionn Murtagh, Jalal Fadili

1689 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum: 2015-10-14
Mått: 186 x 261 x 28 mm
Vikt: 1 000 g
Format: Inbunden
Språk: Engelska
Antal sidor: 428
Upplaga: 2
Förlag: Cambridge University Press
ISBN: 9781107088061

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Mer information

Jean-Luc Starck is Senior Scientist at the Institute of Research into the Fundamental Laws of the Universe, Commissariat ... l'énergie atomique, Saclay, France. 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.

Recensioner i media

Review of previous edition: 'One of the main virtues of this book is the expert insight that the authors provide into several design and algorithmic choices that one can face when solving practical problems. The authors give some guidance into understanding how sparsity helps in signal and image processing, what some benefits of overcomplete representations are, when to use isotropic wavelets for image processing, why morphological diversity can be helpful, and how to choose between analysis and synthesis priors for regularization in inverse problems.' Michael B. Wakin, IEEE Signal Processing Magazine

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