Computed tomography reconstruction using deep image prior and learned reconstruction methods

DOI10.1088/1361-6420/aba415zbMath1460.68093arXiv2003.04989OpenAlexW3040793855MaRDI QIDQ5123693

Daniel Otero Baguer, Maximilian Schmidt, Johannes Leuschner

Publication date: 29 September 2020

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.04989

zbMATH Keywords

neural networkscomputed tomographyinverse problemsdeep learningdeep image prior

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computing methodologies for image processing (68U10) Biomedical imaging and signal processing (92C55)

Related Items (12)

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Uses Software

• PyTorch
• Adam
• GitHub
• U-Net
• dival
• compsensing_dip
• DeepRED
• DeepAdverserialRegulariser
• NETT
• LoDoPaB-CT

Cites Work

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• Regularization by architecture: a deep prior approach for inverse problems
• The Mathematics of Computerized Tomography
• Ideal spatial adaptation by wavelet shrinkage
• Solving ill-posed inverse problems using iterative deep neural networks
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Deep null space learning for inverse problems: convergence analysis and rates
• NETT: solving inverse problems with deep neural networks
• Solving inverse problems using data-driven models
• A convergence rates result for Tikhonov regularization in Banach spaces with non-smooth operators
• Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography

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