Search Results for author:
Found 27 papers, 15 papers with code
Reconstruction for Sparse View Tomography of Long Objects Applied to Imaging in the Wood Industry
In the wood industry, logs are commonly quality screened by discrete X-ray scans on a moving conveyor belt from a few source positions.
Riemannian geometry for efficient analysis of protein dynamics data
First, it comes with a rich structure to account for a wide range of geometries that can be modelled after an energy landscape.
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
Finding suitable preconditioners to accelerate iterative solution methods, such as the conjugate gradient method, is an active area of research.
Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation
On the contrary, we propose a scalable and intuitive framework to calibrate ensembles of deep learning models to produce uncertainty quantification measurements that approximate the classification probability.
Spectral decomposition of atomic structures in heterogeneous cryo-EM
The dataset contains noisy tomographic projections of the electrostatic potential of the macromolecule, taken from different viewing directions, and in the heterogeneous case, each image corresponds to a different conformation of the macromolecule.
Deep Learning for Material Decomposition in Photon-Counting CT
In this work, we present a novel deep-learning solution for material decomposition in PCCT, based on an unrolled/unfolded iterative network.
Learned reconstruction methods with convergence guarantees
In recent years, deep learning has achieved remarkable empirical success for image reconstruction.
3D helical CT Reconstruction with a Memory Efficient Learned Primal-Dual Architecture
To the best of our knowledge, this work is the first to apply an unrolled deep learning architecture for reconstruction on full-sized clinical data, like those in the Low dose CT image and projection data set (LDCT).
Invertible Learned Primal-Dual
We propose invertible Learned Primal-Dual as a method for tomographic image reconstruction.
Deep learning based dictionary learning and tomographic image reconstruction
This work presents an approach for image reconstruction in clinical low-dose tomography that combines principles from sparse signal processing with ideas from deep learning.
Deep Microlocal Reconstruction for Limited-Angle Tomography
We present a deep learning-based algorithm to jointly solve a reconstruction problem and a wavefront set extraction problem in tomographic imaging.
End-to-end reconstruction meets data-driven regularization for inverse problems
We propose an unsupervised approach for learning end-to-end reconstruction operators for ill-posed inverse problems.
Adversarially learned iterative reconstruction for imaging inverse problems
In numerous practical applications, especially in medical image reconstruction, it is often infeasible to obtain a large ensemble of ground-truth/measurement pairs for supervised learning.
Learned convex regularizers for inverse problems
We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.
Shearlets as Feature Extractor for Semantic Edge Detection: The Model-Based and Data-Driven Realm
This is based on the fact that edges in images contain most of the semantic information.
Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation
We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm.
Multi-Scale Learned Iterative Reconstruction
Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models.
Extraction of digital wavefront sets using applied harmonic analysis and deep neural networks
Microlocal analysis provides deep insight into singularity structures and is often crucial for solving inverse problems, predominately, in imaging sciences.
A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging
This model consists of two components, one for conducting modified static image reconstruction, and the other performs sequentially indirect image registration.
Deep Bayesian Inversion
Characterizing statistical properties of solutions of inverse problems is essential for decision making.
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem.
Data-driven nonsmooth optimization
In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function.
Optimization and Control 90C25 (Primary) 68T05, 47H05 (Secondary)
Adversarial Regularizers in Inverse Problems
Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods.
Learning to solve inverse problems using Wasserstein loss
We propose using the Wasserstein loss for training in inverse problems.
Learned Primal-dual Reconstruction
We propose the Learned Primal-Dual algorithm for tomographic reconstruction.
Indirect Image Registration with Large Diffeomorphic Deformations
The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations.
Solving ill-posed inverse problems using iterative deep neural networks
We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators.
