Sparse Image and Signal Processing

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 Côte 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. Fionn Murtagh directs Ireland's Science Foundation funding programs in Information and Communications Technologies and Energy. He holds a Ph.D. from the Université Paris 6 and a habilitation from Université 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. Jalal M. Fadili graduated from the Ecole Nationale Supérieure d'Ingénieurs (ENSI) de Caen, France, and received MSc and Ph.D. degrees in signal and image processing from the University of Caen. He was a Research Associate with the University of Cambridge (McDonnell–Pew Fellow) from 1999 to 2000. He has been an Associate Professor of signal and image processing since September 2001 at ENSI. He was a visitor at several universities (QUT-Australia, Stanford University, CalTech, EPFL). His research interests include mathematical signal and image processing, statistics, optimization theory, and sparse representations.

1. Introduction to the world of sparsity; 2. The wavelet transform; 3. Redundant wavelet transform; 4. Nonlinear multiscale transforms; 5. The ridgelet and curvelet transforms; 6. Sparsity and noise removal; 7. Linear inverse problems; 8. Morphological diversity; 9. Sparse blind source separation; 10. Multiscale geometric analysis on the sphere; 11. Compressed sensing.