This book provides a practical introduction to compressive imaging (with examples and code), an overview of core topics, and a comprehensive, rigorous treatment of the subject. It caters to graduate students, postdocs and faculty in mathematics, computer science, physics and engineering who want to learn about modern imaging techniques.
This book provides a practical introduction to compressive imaging (with examples and code), an overview of core topics, and a comprehensive, rigorous treatment of the subject. It caters to graduate students, postdocs and faculty in mathematics, computer science, physics and engineering who want to learn about modern imaging techniques.
Ben Adcock is Associate Professor of Mathematics at Simon Fraser University. He received the CAIMS/PIMS Early Career Award (2017), an Alfred P. Sloan Research Fellowship (2015) and a Leslie Fox Prize in Numerical Analysis (2011). He has published fifteen conference proceedings, two book chapters and over fifty peer-reviewed journal articles. His work has been published in outlets such as SIAM Review and Proceedings of the National Academy of Sciences, and featured on the cover of SIAM News.
Inhaltsangabe
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 accurate neural networks for compressive imaging 22. Epilogue Appendices: A. Linear Algebra B. Functional analysis C. Probability D. Convex analysis and convex optimization E. Fourier transforms and series F. Properties of Walsh functions and the Walsh transform Notation Abbreviations References Index.
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 accurate neural networks for compressive imaging 22. Epilogue Appendices: A. Linear Algebra B. Functional analysis C. Probability D. Convex analysis and convex optimization E. Fourier transforms and series F. Properties of Walsh functions and the Walsh transform Notation Abbreviations References Index.
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