Compressive Imaging: Structure, Sampling, Learning
B. Adcock & A. C. Hansen
Cambridge University Press (to appear)
Overview
This book presents an in-depth treatment of compressive imaging, a topic which has developed over the last decade and has fundamentally changed the way modern image reconstruction is performed. It commences with a practical introduction to the subject, supported with examples and downloadable code. Next, it introduces core mathematical techniques in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to the most recent trends in compressive imaging, deep learning and neural networks. This highly timely component of the book provides the first readable overview of this nascent topic. Looking toward the next 10-20 years of imaging research, and using both empirical and mathematical insights, this book examines both the potential benefits and the pitfalls of these latest approaches.
Preview
This book will soon be published by Cambridge University Press. In the meantime, selected chapters are available for viewing.
Please send comments or corrections to ben_adcock@sfu.ca.
Contents
Table of contents
1 – Introduction
Part I – The essentials of compressive imaging
2 – Images, transforms and sampling
3 – A short guide to compressive imaging
4 – Techniques for enhancing performance
Part II – Compressed sensing, optimization and wavelets
5 – An introduction to conventional compressed sensing
6 – The LASSO and its cousins
7 – Optimization for compressed sensing
8 – Analysis of optimization algorithms
9 – Wavelets
10 – A taste of wavelet approximation theory
Part III – Compressed sensing with local structure
11 – From global to local
12 – Local structure and nonuniform recovery
13 – Local structure and uniform recovery
14 – Infinite-dimensional compressed sensing
Part IV – Compressed sensing for imaging
15 – Sampling strategies for compressive imaging
16 – Recovery guarantees for wavelet-based compressive imaging
17 – Total variation minimization
Part V – From compressed sensing to deep learning
18 – Neural networks and deep learning
19 – Deep learning for compressive imaging
20 – Accuracy and stability of deep learning for compressive imaging
21 – Stable and accuracy neural networks for compressive imaging
22 – Epilogue
References