Frontmatter

Chapters in this book

1. Frontmatter I
2. Preface V
3. Contents VII
4. Part I: Mathematical aspects of data-driven methods in inverse problems
5. On optimal regularization parameters via bilevel learning 1
6. Learned regularization for inverse problems 39
7. Inverse problems with learned forward operators 73
8. Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging 107
9. Learned reconstruction methods for inverse problems: sample error estimates 163
10. Statistical inverse learning problems with random observations 201
11. General regularization in covariate shift adaptation 245
12. Part II: Applications of data-driven methods in inverse problems
13. Analysis of generalized iteratively regularized Landweber iterations driven by data 273
14. Integration of model- and learning-based methods in image restoration 303
15. Dynamic computerized tomography using inexact models and motion estimation 331
16. Deep Bayesian inversion 359
17. Utilizing uncertainty quantification variational autoencoders in inverse problems with applications in photoacoustic tomography 413
18. Electrical impedance tomography: a fair comparative study on deep learning and analytic-based approaches 437
19. Classification with neural networks with quadratic decision functions 471
20. Index 495