Compressed Sensing

Compressed sensing is an exciting, rapidly growing field which has attracted considerable attention in electrical engineering, applied mathematics, statistics, and computer science. Since its initial introduction several years ago an avalanche of results have been obtained both of a theoretical as well as practical nature, and various conferences, workshops, and special sessions have been dedicated to this growing research field. This book provides the first detailed introduction to the subject, highlighting recent theoretical advances and a range of applications, as well as outlining numerous remaining research challenges. After a thorough review of the basic theory, many cutting-edge advances in the field are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, hardware prototypes, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms, the use of graphical models, and the separation of morphologically distinct data components. Each chapter iswritten by leading researchers in the field, and consistent style and notation are utilized throughout. An extended introductory chapter summarizes the basics of the field so that no prior knowledge is required. Key background information and clear definitions make this book an ideal resource for researchers, graduate students, and practitioners wanting to join this exciting research area. It can also serve as a supplementary textbook for courses on computer vision, coding theory, signal processing, image processing, and algorithms for efficient data processing.