Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction

被引：0

作者：

Piening, Moritz ^{[1
]}

Altekrüger, Fabian ^{[2
]}

Hertrich, Johannes ^{[3
]}

Hagemann, Paul ^{[1
]}

Walther, Andrea ^{[2
]}

Steidl, Gabriele ^{[1
]}

机构：

[1] Institute of Mathematics, Technische Universität Berlin, Berlin, Germany

[2] Department of Mathematics, Humboldt-Universität zu Berlin, Berlin, Germany

[3] Department of Computer Science, University College London, London, United Kingdom

来源：

GAMM Mitteilungen | 2024年 / 47卷 / 04期

关键词：

Compendex;

D O I：

10.1002/gamm.202470002

中图分类号：

学科分类号：

摘要：

Computerized tomography - Deep neural networks - Differential equations - Image reconstruction - Learning systems - Medical imaging - Medical problems - Monte Carlo methods - Optical resolving power - Uncertainty analysis

引用

共 3 条

[1] Model-based federated learning for accurate MR image reconstruction from undersampled k-space data
Wu, Ruoyou
Li, Cheng
Zou, Juan
Liang, Yong
Wang, Shanshan
Computers in Biology and Medicine, 2024, 180
[2] Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models
Wodzinski, Marek
Kwarciak, Kamil
Daniol, Mateusz
Hemmerling, Daria
Computers in Biology and Medicine, 2024, 182
[3] Feasibility study for image reconstruction in circular digital tomosynthesis (CDTS) from limited-scan angle data based on compressed-sensing theory
Department of Radiological Science, ITOMO and VYSION Research Center, Yonsei University, Wonju
220-710, Korea, Republic of
Nucl Instrum Methods Phys Res Sect A, (161-166):

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