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. MATLABAr and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications.Wavelets and Related Geometric Multiscale Analysis Jean-Luc Starck, Fionn Murtagh, Jalal Fadili ... are available for different transforms described in this chapter at http://www.cosmostat.org/software.html: Fast 3DCurvelets: Matlab code for 3D Fast curvelets. ... Other resources include: http://www.flaglets.org: For the flaglet wavelet transform on the ball. http://www.curvelet.org: For the Curvelab ... Applications to denoising, image decomposition and inpainting on the sphere were given.
|Title||:||Sparse Image and Signal Processing|
|Author||:||Jean-Luc Starck, Fionn Murtagh, Jalal Fadili|
|Publisher||:||Cambridge University Press - 2015-10-14|