Deep Learning for Computational Imaging

Texte du rabat

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.

Contenu

• 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