Starck, Jean-Luc, <br/><br/>
Sparse Image and Signal Processing :  Wavelets and Related Geometric Multiscale Analysis / 
 Sparse Image & Signal Processing 
Jean-Luc Starck, Fionn Murtagh, Jalal Fadili. 
 - 2nd ed. 
 - 1 online resource (428 pages) :  digital, PDF file(s). 
<br/><br/> Title from publisher's bibliographic system (viewed on 07 Mar 2017). 
<br/><br/> 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. MATLABŪ and IDL code, available online at www.SparseSignalRecipes.info, accompany these methods and all applications. 
<br/><br/><label>ISBN: </label>9781316104514 (ebook) 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Transformations (Mathematics)<br/>Signal processing<br/>Image processing<br/>Sparse matrices<br/>Wavelets (Mathematics)
<br/><br/><label>LC Class. No.: </label>QA601  / .S785 2015 
<br/><br/><label>Dewey Class. No.: </label>621.36/7