Sparse image and signal processing: Wavelets and related geometric multiscale analysis


Starck, Jean-Luc, Murtagh, Fionn and Fadili, Jalal 2016. Sparse image and signal processing: Wavelets and related geometric multiscale analysis. Cambridge University Press.
AuthorsStarck, Jean-Luc, Murtagh, Fionn and Fadili, Jalal

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.

KeywordsMultiresolution methods; Wavelets / curvelets / ridgelets; Compressive sampling; Image and signal processing
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Publication datesJan 2016
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Deposited10 Nov 2016, 15:51
PublisherCambridge University Press
ContributorsCentre d'Etudes Atomiques, Saclay, Ecole Nationale Supérieure de Caen and University of Derby
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