Presents state-of-the-art sparse and multiscale image and signal processing with applications in astronomy, biology, MRI, media, and forensics.
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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.
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Hardcover. Zustand: new. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9781107088061
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Hardcover. Zustand: new. 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9781107088061
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