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dc.contributor.authorStarck, Jean-Lucen
dc.contributor.authorMurtagh, Fionnen
dc.contributor.authorFadili, Jalalen
dc.date.accessioned2016-11-10T15:51:11Z
dc.date.available2016-11-10T15:51:11Z
dc.date.issued2016-01
dc.identifier.citationStarck, J., Murtagh, F., Fadili, J. (2016) 'Sparse image and signal processing: Wavelets and related geometric multiscale analysis', Cambridge: Cambridge University Press, 2nd ed.en
dc.identifier.isbn9781107088061
dc.identifier.urihttp://hdl.handle.net/10545/620804
dc.description.abstractThis 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.
dc.language.isoenen
dc.publisherCambridge University Pressen
dc.relation.urlhttp://www.cambridge.org/gb/academic/subjects/computer-science/computer-graphics-image-processing-and-robotics/sparse-image-and-signal-processing-wavelets-and-related-geometric-multiscale-analysis-2nd-edition?format=HBen
dc.subjectMultiresolution methodsen
dc.subjectWavelets / curvelets / ridgeletsen
dc.subjectCompressive samplingen
dc.subjectImage and signal processingen
dc.titleSparse image and signal processing: Wavelets and related geometric multiscale analysisen
dc.typeBooken
dc.contributor.departmentCentre d'Etudes Atomiques, Saclayen
dc.contributor.departmentEcole Nationale Supérieure de Caenen
dc.contributor.departmentUniversity of Derbyen
html.description.abstractThis 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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