Deep Learning--Based Dictionary Learning and Tomographic Image Reconstruction
From MaRDI portal
Publication:5056920
DOI10.1137/21M1445697zbMath1499.62413arXiv2108.11730OpenAlexW3196050896MaRDI QIDQ5056920
Jevgenija Rudzusika, Thomas Köhler, Ozan Öktem
Publication date: 8 December 2022
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.11730
Applications of statistics to biology and medical sciences; meta analysis (62P10) Artificial neural networks and deep learning (68T07) Biomedical imaging and signal processing (92C55) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- A folded Laplace distribution
- Iterative thresholding for sparse approximations
- Variational methods in imaging
- On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
- A general scheme for constructing inversion algorithms for cone beam CT
- Variational networks: an optimal control approach to early stopping variational methods for image restoration
- A convex variational model for learning convolutional image atoms from incomplete data
- 3PI algorithms for helical computer tomography
- Mathematical Methods in Image Reconstruction
- A Variational Method for Learning Sparse and Overcomplete Representations
- A TGV-Based Framework for Variational Image Decompression, Zooming, and Reconstruction. Part II: Numerics
- Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems
- Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
- Sparse and Redundant Representations
- Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?
- A Sobolev Space Analysis of Picture Reconstruction
- $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
- First-Order Methods in Optimization
- Deep Convolutional Neural Network for Inverse Problems in Imaging
- Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
- Inversion of cone-beam data and helical tomography
- Compressive Imaging: Structure, Sampling, Learning
- Convolutional Analysis Operator Learning: Acceleration and Convergence
- NETT: solving inverse problems with deep neural networks
- Computed tomography reconstruction using deep image prior and learned reconstruction methods
- Deep synthesis network for regularizing inverse problems
- On the Uniqueness and Stability of Dictionaries for Sparse Representation of Noisy Signals
- Solving inverse problems using data-driven models
- Deep Dictionary Learning: A PARametric NETwork Approach
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution
- Stable signal recovery from incomplete and inaccurate measurements
- Total Generalized Variation
