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Learned Iterative Reconstruction
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).ORCID iD: 0000-0001-9928-3407
Number of Authors: 12023 (English)In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision, Springer Nature , 2023, p. 751-771Chapter in book (Other academic)
Abstract [en]

Learned iterative reconstruction methods have recently emerged as a powerful tool to solve inverse problems. These deep learning techniques for image reconstruction achieve remarkable speed and accuracy by combining hard knowledge about the physics of the image formation process, represented by the forward operator, with soft knowledge about how the reconstructions should look like, represented by deep neural networks. A diverse set of such methods have been proposed, and this chapter seeks to give an overview of their similarities and differences, as well as discussing some of the commonly used methods to improve their performance.

Place, publisher, year, edition, pages
Springer Nature , 2023. p. 751-771
Keywords [en]
Architectures, Deep Learning, Inverse Problems, Iterative reconstruction
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:kth:diva-333013DOI: 10.1007/978-3-030-98661-2_67Scopus ID: 2-s2.0-85161810207OAI: oai:DiVA.org:kth-333013DiVA, id: diva2:1784258
Note

Part of ISBN 9783030986612 9783030986605

QC 20230725

Available from: 2023-07-25 Created: 2023-07-25 Last updated: 2023-07-25Bibliographically approved

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