Posterior-Variance–Based Error Quantification for Inverse Problems in Imaging
Author:
Affiliation:
1. Institute for Computer Graphics and Vision, Graz University of Technology, Graz 8010, Austria.
2. Institute of Mathematics and Scientific Computing, University of Graz, Graz A-8010, Austria.
Funder
National Institutes of Health
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Reference49 articles.
1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges
2. MoDL: Model-Based Deep Learning Architecture for Inverse Problems
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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Robustness and exploration of variational and machine learning approaches to inverse problems: An overview;GAMM-Mitteilungen;2024-08-07
2. Neural‐network‐based regularization methods for inverse problems in imaging;GAMM-Mitteilungen;2024-07-18
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