Jean-Luc Starck

Sparse Image and Signal Processing: Wavelets and Related Geometric Multiscale Analysis, Second Edition Jean-Luc Starck 2 Revised edition

Marc Notes: Includes bibliographical references and index. Review Quotes: Review of previous edition: 'One of the main virtues of this book is the expert insight that the authors provide into several design and algorithmic choices that one can face when solving practical problems. The authors give some guidance into understanding how sparsity helps in signal and image processing, what some benefits of overcomplete representations are, when to use isotropic wavelets for image processing, why morphological diversity can be helpful, and how to choose between analysis and synthesis priors for regularization in inverse problems.' Michael B. Wakin, IEEE Signal Processing MagazineReview Quotes: Review of previous edition: 'The book's contents are well prepared for graduate-level students or advanced undergraduates who work in the field of image and signal processing or computer science. The book is also an indispensable resource for professionals looking to adopt innovative concepts for improving the performance of image processing.' Yan Gao, Optics and Photonics NewsReview Quotes: Review of previous edition: 'This is an excellent book devoted to an important domain of contemporary science.' D. Stanomir, Mathematical ReviewsReview Quotes: Review of previous edition: 'A welcome addition to the image processing library.' T. Kubota, Computing ReviewsTable of Contents: 1. Introduction to the world of sparsity; 2. The wavelet transform; 3. Redundant wavelet transform; 4. Nonlinear multiscale transforms; 5. Multiscale geometric transforms; 6. Sparsity and noise removal; 7. Linear inverse problems; 8. Morphological diversity; 9. Sparse blind source separation; 10. Dictionary learning; 11. Three-dimensional sparse representations; 12. Multiscale geometric analysis on the sphere; 13. Compressed sensing; 14. This book's take-home message. Publisher Marketing: 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(r) and IDL code, available online at www. SparseSignalRecipes.info, accompany these methods and all applications.

Contributor Bio:  Starck, Jean-Luc Jean-Luc Starck is a researcher at the Institute of Research into the Fundamental Laws of the Universe (IRFU), CEA-Saclay. He holds a Ph. D. from the University of Nice-Sophia Antipolis and Observatory of Cote d'Azur and a habilitation degree from the University Paris XI. He is a former visiting researcher at the European Southern Observatory (ESO), UCLA, and the Statistics Department at Stanford University. His research interests include image processing, statistical methods in astrophysics, and cosmology. He is also author of two books, entitled Image Processing and Data Analysis: The Multiscale Approach and Astronomical Image and Data Analysis. Contributor Bio:  Murtagh, Fionn Fionn Murtagh directs Ireland's Science Foundation funding programs in Information and Communications Technologies and Energy. He holds a Ph. D. from the Universite Paris 6 and a habilitation from Universite de Strasbourg. Murtagh held professorial chairs at the University of Ulster, Queen's University Belfast, and now at Royal Holloway, University of London. He is a Fellow of the International Association for Pattern Recognition, a Fellow of the British Computer Society, and an elected Member of the Royal Irish Academy. Contributor Bio:  Fadili, Jalal Jalal M. Fadili has been full professor at Institut Universitaire de France since October 2013. His research interests include signal and image processing, statistics, optimization theory, and low-complexity regularization. He is a member of the editorial boards of several journals.