Compressed Sensing: Theory and Applications

Compressed sensing is an exciting, rapidly growing field, attracting considerable attention in electrical engineering, applied mathematics, statistics and computer science. 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 techniques are presented, including advanced signal modeling, sub-Nyquist sampling of analog signals, non-asymptotic analysis of random matrices, adaptive sensing, greedy algorithms and use of graphical models. All chapters are written by leading researchers in the field, and consistent style and notation are utilized throughout. Key background information and clear definitions make this 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.

Table of Contents

1: Introduction to compressed sensing
2: Second-generation sparse modeling: structured and collaborative signal analysis
3: Xampling: compressed sensing of analog signals
4: Sampling at the rate of innovation: theory and applications
5: Introduction to the non-asymptotic analysis of random matrices
6: Adaptive sensing for sparse recovery
7: Fundamental thresholds in compressed sensing: a high-dimensional geometry approach
8: Greedy algorithms for compressed sensing
9: Graphical models concepts in compressed sensing
10: Finding needles in compressed haystacks
11: Data separation by sparse representations
12: Face recognition by sparse representation