Compressed Sensing: Theory and Applications

doi:10.1017/CBO9780511794308

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 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.

'… a charming encouragement to fascinating scientific adventure for talented students. Also … a solid reference platform for researchers in many fields.'

Artur Przelaskowski Source: IEEE Communications Magazine

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Contents

Frontmatter
pp i-iv
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Contents
pp v-vi
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List of contributors
pp vii-viii
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Preface
pp ix-xii
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1 - Introduction to compressed sensing
pp 1-64
- By Mark A. Davenport, Stanford University, USA, Marco F. Duarte, Duke University, Durham, USA, Yonina C. Eldar, Stanford University, USA, Gitta Kutyniok, Technische Universität Berlin, Germany
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2 - Second-generation sparse modeling: structured and collaborative signal analysis
pp 65-87
- By Alexey Castrodad, University of Minnesota, Minneapolis, USA, Ignacio Ramirez, University of Minnesota, Minneapolis, USA, Guillermo Sapiro, University of Minnesota, Minneapolis, USA, Pablo Sprechmann, University of Minnesota, Minneapolis, USA, Guoshen Yu, University of Minnesota, Minneapolis, USA
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3 - Xampling: compressed sensing of analog signals
pp 88-147
- By Moshe Mishali, Technion-Israel Institute of Technology, Israel, Yonina C. Eldar, Stanford University, USA
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4 - Sampling at the rate of innovation: theory and applications
pp 148-209
- By Jose Antonio Urigüen, Imperial College London, UK, Yonina C. Eldar, Stanford University, USA, Pier Luigi Dragotti, Imperial College London, UK, Zvika Ben-Haim, Technion-Israel Institute of Technology, Israel
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5 - Introduction to the non-asymptotic analysis of random matrices
pp 210-268
- By Roman Vershynin, University of Michigan, USA
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6 - Adaptive sensing for sparse recovery
pp 269-304
- By Jarvis Haupt, University of Minnesota, USA, Robert Nowak, University of Wisconsin, USA
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7 - Fundamental thresholds in compressed sensing: a high-dimensional geometry approach
pp 305-347
- By Weiyu Xu, Cornell University, USA, Babak Hassibi, California Institute of Technology, USA
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8 - Greedy algorithms for compressed sensing
pp 348-393
- By Thomas Blumensath, University of Oxford, UK, Michael E. Davies, University of Edinburgh, UK, Gabriel Rilling, University of Edinburgh, UK
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9 - Graphical models concepts in compressed sensing
pp 394-438
- By Andrea Montanari, Stanford University, USA
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10 - Finding needles in compressed haystacks
pp 439-484
- By Robert Calderbank, Duke University, Durham, USA, Sina Jafarpour, Princeton University, USA
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11 - Data separation by sparse representations
pp 485-514
- By Gitta Kutyniok, Technische Universität Berlin, Germany
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12 - Face recognition by sparse representation
pp 515-539
- By Arvind Ganesh, University of Illinois, USA, Andrew Wagner, University of Illinois, USA, Zihan Zhou, University of Illinois, USA, Allen Y. Yang, University of California, USA, Yi Ma, Microsoft Research Asia, China, John Wright, Microsoft Research Asia, China
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Index
pp 540-544
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Compressed Sensing

Theory and Applications

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

List of contributors
pp vii-viii

Preface
pp ix-xii

1 - Introduction to compressed sensing
pp 1-64

2 - Second-generation sparse modeling: structured and collaborative signal analysis
pp 65-87

3 - Xampling: compressed sensing of analog signals
pp 88-147

4 - Sampling at the rate of innovation: theory and applications
pp 148-209

5 - Introduction to the non-asymptotic analysis of random matrices
pp 210-268

6 - Adaptive sensing for sparse recovery
pp 269-304

7 - Fundamental thresholds in compressed sensing: a high-dimensional geometry approach
pp 305-347

8 - Greedy algorithms for compressed sensing
pp 348-393

9 - Graphical models concepts in compressed sensing
pp 394-438

10 - Finding needles in compressed haystacks
pp 439-484

11 - Data separation by sparse representations
pp 485-514

12 - Face recognition by sparse representation
pp 515-539

Index
pp 540-544

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Compressed Sensing

Theory and Applications

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