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Found 23 papers, 11 papers with code
Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches
A suite of performance metrics is employed to assess the efficacy of these methods.
Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems
Denoising diffusion models have emerged as the go-to framework for solving inverse problems in imaging.
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography.
Solving Elliptic Optimal Control Problems using Physics Informed Neural Networks
In this work, we present and analyze a numerical solver for optimal control problems (without / with box constraint) for linear and semilinear second-order elliptic problems.
On the Approximation of Bi-Lipschitz Maps by Invertible Neural Networks
Furthermore, we develop an approach for approximating bi-Lipschitz maps on infinite-dimensional spaces that simultaneously approximate the forward and inverse maps, by combining model reduction with principal component analysis and INNs for approximating the reduced map, and we analyze the overall approximation error of the approach.
Electrical Impedance Tomography with Deep Calderón Method
Electrical impedance tomography (EIT) is a noninvasive medical imaging modality utilizing the current-density/voltage data measured on the surface of the subject.
Conductivity Imaging from Internal Measurements with Mixed Least-Squares Deep Neural Networks
In this work we develop a novel approach using deep neural networks to reconstruct the conductivity distribution in elliptic problems from one measurement of the solution over the whole domain.
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless.
Image Reconstruction via Deep Image Prior Subspaces
Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data.
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method
Extensive numerical experiments in two- and multi-dimensional spaces with point sources, line sources or their combinations are presented to illustrate the efficiency of the proposed approach, and a comparative study with several existing approaches based on neural networks is also given, which shows clearly its competitiveness for the specific class of problems.
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction.
Imaging Conductivity from Current Density Magnitude using Neural Networks
Conductivity imaging represents one of the most important tasks in medical imaging.
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment.
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
A Probabilistic Deep Image Prior over Image Space
The deep image prior regularises under-specified image reconstruction problems by reparametrising the target image as the output of a CNN.
Conditional Variational Autoencoder for Learned Image Reconstruction
Learned image reconstruction techniques using deep neural networks have recently gained popularity, and have delivered promising empirical results.
Unsupervised Knowledge-Transfer for Learned Image Reconstruction
Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities.
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Image reconstruction methods based on deep neural networks have shown outstanding performance, equalling or exceeding the state-of-the-art results of conventional approaches, but often do not provide uncertainty information about the reconstruction.
Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e. g., deep neural networks.
Probabilistic Residual Learning for Aleatoric Uncertainty in Image Restoration
Aleatoric uncertainty is an intrinsic property of ill-posed inverse and imaging problems.
Variational Gaussian Approximation for Poisson Data
In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior.
A Primal Dual Active Set with Continuation Algorithm for the \ell^0-Regularized Optimization Problem
We develop a primal dual active set with continuation algorithm for solving the \ell^0-regularized least-squares problem that frequently arises in compressed sensing.
A Unified Primal Dual Active Set Algorithm for Nonconvex Sparse Recovery
In this paper, we consider the problem of recovering a sparse signal based on penalized least squares formulations.
