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This textbook offers an introduction to deep learning for solving inverse problems. It introduces deep neural networks and deep neural network based signal and image reconstruction techniques. It discusses robustness aspects, how to evaluate and test different methods, and data-centric aspects.
List of contents
- 1: Introduction
- 2: Solving inverse problems with optimization tasks
- 3: Solving optimization problems
- 4: Sparse modelling
- 5: Plug-and-play methods
- 6: Learning to solve inverse problems end-to-end
- 7: Unrolled neural networks
- 8: Self-supervised learning
- 9: Signal reconstruction via imposing generative priors
- 10: Diffusion models
- 11: Signal reconstruction with un-trained neural networks
- 12: Coordinate-based multi-layer perceptrons
- 13: Robustness to perturbations
- 14: Datasets and evaluation of image reconstruction methods
- 15: Advanced reconstruction problems
- 16: Mathematical background
About the author
Reinhard Heckel is a Professor of Machine Learning (Tenured Associate Professor) at the Department of Computer Engineering at the Technical University of Munich (TUM), and adjunct faculty at Rice University, where he was an assistant professor of Electrical and Computer Engineering from 2017-2019. Before that, he was a postdoctoral researcher in the Berkeley Artificial Intelligence Research Lab at UC Berkeley, and before that a researcher at IBM Research Zurich. He completed his PhD in 2014 at ETH Zurich and was a visiting PhD student at Stanfords University's Statistics Department. Reinhard's work is centered on machine learning, artificial intelligence, and information processing, with a focus on developing algorithms and foundations for deep learning, particularly for medical imaging, on establishing mathematical and empirical underpinnings for machine learning, and on the utilization of DNA as a digital information technology.
Summary
This textbook offers an introduction to deep learning for solving inverse problems. It introduces deep neural networks and deep neural network based signal and image reconstruction techniques. It discusses robustness aspects, how to evaluate and test different methods, and data-centric aspects.
