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. MATLABÂ and IDL code available online at www. SparseSignalRecipes. info accompany these methods and all applications. Language: English