Deep Learning for Computational Imaging

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

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.

Product details

• Authors: Prof Reinhard (Professor of Machine Learni Heckel, Reinhard Heckel
• Publisher: Oxford University Press
• Languages: English
• Product format: Paperback / Softback
• Released: 30.04.2025
• EAN: 9780198947189
• ISBN: 978-0-19-894718-9
• No. of pages: 240
• Subjects: Natural sciences, medicine, IT, technology > Technology > Electronics, electrical engineering, communications engineering; MATHEMATICS / Applied, TECHNOLOGY & ENGINEERING / Electronics / General, TECHNOLOGY & ENGINEERING / Telecommunications, COMPUTERS / Machine Theory, Mathematical theory of computation, Applied mathematics, Electronics & Communications Engineering, Electronics and communications engineering