Neural‐network‐based regularization methods for inverse problems in imaging
Abstract: This review provides an introduction to—and overview of—the current state of the art in neural‐network based regularization methods for inverse problems in imaging. It aims to introduce readers with a solid knowledge in applied mathematics and a basic understanding of neural networks to different concepts of applying neural networks for regularizing inverse problems in imaging. Distinguishing features of this review are, among others, an easily accessible introduction to learned generators and learned priors, in particular diffusion models, for inverse problems, and a section focusing explicitly on existing results in function space analysis of neural‐network‐based approaches in this context.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Neural‐network‐based regularization methods for inverse problems in imaging ; day:18 ; month:07 ; year:2024 ; extent:35
GAMM-Mitteilungen / Gesellschaft für Angewandte Mathematik und Mechanik ; (18.07.2024) (gesamt 35)
- Urheber
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Habring, Andreas
Holler, Martin
- DOI
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10.1002/gamm.202470004
- URN
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urn:nbn:de:101:1-2407191418164.770313256960
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:44 MESZ
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Beteiligte
- Habring, Andreas
- Holler, Martin
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