Data driven regularization by projection
From MaRDI portal
Publication:5139332
DOI10.1088/1361-6420/abb61bzbMath1502.65030arXiv1909.11570OpenAlexW2976576775MaRDI QIDQ5139332
A. Aspri, Yury Korolev, Otmar Scherzer
Publication date: 8 December 2020
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1909.11570
inverse problemsGram-Schmidt orthogonalizationvariational regularizationregularization by projectiondata driven regularization
Related Items
Operator Shifting for General Noisy Matrix Systems ⋮ Discretization of parameter identification in PDEs using neural networks ⋮ Adaptive Tikhonov strategies for stochastic ensemble Kalman inversion ⋮ A machine learning approach to optimal Tikhonov regularization I: Affine manifolds ⋮ Gauss-Newton method for solving linear inverse problems with neural network coders ⋮ Convergence Rates for Learning Linear Operators from Noisy Data ⋮ Operator shifting for noisy elliptic systems ⋮ Invertible residual networks in the context of regularization theory for linear inverse problems ⋮ A Fast Data-Driven Iteratively Regularized Method with Convex Penalty for Solving Ill-Posed Problems
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nonlinear total variation based noise removal algorithms
- Variational methods in imaging
- Nonconvergence results for the application of least-squares estimation to ill-posed problems
- Image recovery via total variation minimization and related problems
- Mathematical Methods in Image Reconstruction
- Finite-dimensional approximation of tikhonov regularized solutions of non-linear ill-posed problems
- Graph Implementations for Nonsmooth Convex Programs
- Discretization of variational regularization in Banach spaces
- Sharp converse results for the regularization error using distance functions
- Deep Learning for Trivial Inverse Problems
- Inverse problems in astronomical adaptive optics
- The seismic reflection inverse problem
- Analysis of bounded variation penalty methods for ill-posed problems
- A Variational Method in Image Recovery
- 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
- Convergence rates of convex variational regularization
- Tomography
- NETT: solving inverse problems with deep neural networks
- A Data-Driven Iteratively Regularized Landweber Iteration
- Statistical Numerical Approximation
- Modern regularization methods for inverse problems
- Solving inverse problems using data-driven models
- Generalized Convergence Rates Results for Linear Inverse Problems in Hilbert Spaces
- Nyström type subsampling analyzed as a regularized projection
- Learning the invisible: a hybrid deep learning-shearlet framework for limited angle computed tomography
- Convergence rates for Tikhonov regularization from different kinds of smoothness conditions
- Inverse problems for partial differential equations
- Inverse acoustic and electromagnetic scattering theory
- Training neural networks with noisy data as an ill-posed problem
