Key points are not available for this paper at this time.
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.
Building similarity graph...
Analyzing shared references across papers
Loading...
Habring et al. (Thu,) studied this question.
www.synapsesocial.com/papers/68e5fc83b6db643587590dbe — DOI: https://doi.org/10.1002/gamm.202470004
Andreas Habring
Martin Höller
GAMM-Mitteilungen
University of Graz
Building similarity graph...
Analyzing shared references across papers
Loading...