Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis

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9781107088061: Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis

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.

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Book Description:

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.

About the Author:

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

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2015. Hardback. Book Condition: New. 2nd Revised edition. Language: English . Brand New Book. 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 (R) and IDL code, available online at accompany these methods and all applications. Bookseller Inventory # AAA9781107088061

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2015. Hardback. Book Condition: New. 2nd Revised edition. Language: English . Brand New Book. 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 (R) and IDL code, available online at accompany these methods and all applications. Bookseller Inventory # AAA9781107088061

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Book Description Cambridge University Press. Hardback. Book Condition: new. BRAND NEW, Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis (2nd Revised edition), Jean-Luc Starck, Fionn Murtagh, Jalal M. Fadili, 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(R) and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications. Bookseller Inventory # B9781107088061

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