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09/19/2023
Single-Image based unsupervised joint segmentation and denoising
In this work, we develop an unsupervised method for the joint segmentati...
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09/12/2023
Data-proximal null-space networks for inverse problems
Inverse problems are inherently ill-posed and therefore require regulari...
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08/29/2023
Convergence of non-linear diagonal frame filtering for regularizing inverse problems
Inverse problems are core issues in several scientific areas, including ...
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06/26/2023
Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images
Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imagi...
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06/02/2023
Convergence analysis of equilibrium methods for inverse problems
Recently, the use of deep equilibrium methods has emerged as a new appro...
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05/04/2023
Translation invariant diagonal frame decomposition for the Radon transform
In this article, we address the challenge of solving the ill-posed recon...
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04/23/2023
A complementary ℓ^1-TV reconstruction algorithm for limited data CT
In a variety of tomographic applications, data cannot be fully acquired,...
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04/14/2023
Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction
Reconstructing an image from noisy and incomplete measurements is a cent...
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02/17/2023
Convergence rates for critical point regularization
Tikhonov regularization involves minimizing the combination of a data di...
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02/04/2023
Variational multichannel multiclass segmentationusing unsupervised lifting with CNNs
We propose an unsupervised image segmentation approach, that combines a ...
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12/13/2022
Plug-and-Play image reconstruction is a convergent regularization method
Non-uniqueness and instability are characteristic features of image reco...
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08/17/2022
Translation invariant diagonal frame decomposition of inverse problems and their regularization
Solving inverse problems is central to a variety of important applicatio...
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07/09/2022
Unsupervised Joint Image Transfer and Uncertainty Quantification using Patch Invariant Networks
Unsupervised image transfer enables intra- and inter-modality transfer f...
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06/09/2022
Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks
Sparsity-based methods have a long history in the field of signal proces...
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03/04/2022
Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks
The concept of sparsity has been extensively applied for regularization ...
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02/22/2022
Feature reconstruction from incomplete tomographic data without detour
In this paper, we consider the problem of feature reconstruction from in...
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02/09/2022
A Joint Variational Multichannel Multiphase Segmentation Framework
In this paper, we propose a variational image segmentation framework for...
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01/28/2022
Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
Real-time estimation of actual object depth is a module that is essentia...
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01/02/2022
Compressed Sensing of Indirect Data
Compressed sensing (CS) is a powerful tool for reducing the amount of da...
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09/01/2021
Combining reconstruction and edge detection in computed tomography
We present two methods that combine image reconstruction and edge detect...
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06/10/2021
A Variational View on Statistical Multiscale Estimation
We present a unifying view on various statistical estimation techniques ...
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03/31/2021
Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs
Real-time estimation of actual environment depth is an essential module ...
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03/15/2021
Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines
Digitalization offers a large number of promising tools for large intern...
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02/01/2021
An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction
Purpose: Iterative Convolutional Neural Networks (CNNs) which resemble u...
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11/06/2020
Discretization of learned NETT regularization for solving inverse problems
Deep learning based reconstruction methods deliver outstanding results f...
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09/01/2020
PiNet: Deep Structure Learning using Feature Extraction in Trained Projection Space
Over the last decade of machine learning, convolutional neural networks ...
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08/14/2020
Regularization of Inverse Problems by Filtered Diagonal Frame Decomposition
The characteristic feature of inverse problems is their instability with...
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07/29/2020
Multi-Scale Factorization of the Wave Equation with Application to Compressed Sensing Photoacoustic Tomography
By performing a large number of spatial measurements, high spatial resol...
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06/06/2020
Regularization of Inverse Problems by Neural Networks
Inverse problems arise in a variety of imaging applications including co...
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04/20/2020
Sparse aNETT for Solving Inverse Problems with Deep Learning
We propose a sparse reconstruction framework (aNETT) for solving inverse...
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03/25/2020
Data-consistent neural networks for solving nonlinear inverse problems
Data assisted reconstruction algorithms, incorporating trained neural ne...
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02/25/2020
The conical Radon transform with vertices on triple lines
We study the inversion of the conical Radon which integrates a function ...
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02/10/2020
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
In this work, we propose an iterative reconstruction scheme (ALONE - Ada...
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02/01/2020
Deep synthesis regularization of inverse problems
Recently, a large number of efficient deep learning methods for solving ...
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12/19/2019
Neural Networks-based Regularization of Large-Scale Inverse Problems in Medical Imaging
In this paper we present a generalized Deep Learning-based approach to s...
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10/23/2019
Random 2.5D U-net for Fully 3D Segmentation
Convolutional neural networks are state-of-the-art for various segmentat...
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09/20/2019
Sparse regularization of inverse problems by biorthogonal frame thresholding
We analyze sparse frame based regularization of inverse problems by mean...
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08/08/2019
Sparse ℓ^q-regularization of inverse problems with deep learning
We propose a sparse reconstruction framework for solving inverse problem...
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02/21/2019
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
Hepatocellular carcinoma (HCC) is the most common type of primary liver ...
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02/01/2019
Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
Convolutional neural networks are state-of-the-art for various segmentat...
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02/28/2018
