347 citations · 642 across the 16 of their papers we have counts for
19 papers
Learning Greens Operators through Hierarchical Neural Networks Inspired by the Fast Multipole Method
Emilio McAllister Fognini, Marta M. Betcke, Ben T. Cox
The Fast Multipole Method (FMM) is an efficient numerical algorithm for computation of long-ranged forces in -body problems within gravitational and electrostatic fields. This m…
Generative imaging for radio interferometry with fast uncertainty quantification
Matthijs Mars, Tobías I. Liaudat, Jessica J. Whitney +2
With the rise of large radio interferometric telescopes, particularly the SKA, there is a growing demand for computationally efficient image reconstruction techniques. Existing rec…
Parallel-in-Time Solutions with Random Projection Neural Networks
Marta M. Betcke, Lisa Maria Kreusser, Davide Murari
This paper considers one of the fundamental parallel-in-time methods for the solution of ordinary differential equations, Parareal, and extends it by adopting a neural network as a…
Learned radio interferometric imaging for varying visibility coverage
Matthijs Mars, Marta M. Betcke, Jason D. McEwen
With the next generation of interferometric telescopes, such as the Square Kilometre Array (SKA), the need for highly computationally efficient reconstruction techniques is particu…
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
Tobías I. Liaudat, Matthijs Mars, Matthew A. Price +3
Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensi…
Learned Interferometric Imaging for the SPIDER Instrument
Matthijs Mars, Marta M. Betcke, Jason D. McEwen
The Segmented Planar Imaging Detector for Electro-Optical Reconnaissance (SPIDER) is an optical interferometric imaging device that aims to offer an alternative to the large space…
On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector
Bolin Pan, Marta M. Betcke
In photoacoustic tomography (PAT) with flat sensor, we routinely encounter two types of limited data. The first is due to using a finite sensor and is especially perceptible if the…
Photoacoustic Reconstruction Using Sparsity in Curvelet Frame: Image versus Data Domain
Bolin Pan, Simon R. Arridge, Felix Lucka +5
Curvelet frame is of special significance for photoacoustic tomography (PAT) due to its sparsifying and microlocalisation properties. We derive a one-to-one map between wavefront d…
Hamilton-Green solver for the forward and adjoint problems in photoacoustic tomography
Francesc Rullan, Marta M. Betcke
The majority of the solvers for the acoustic problem in Photoacoustic Tomography (PAT) rely on full solution of the wave equation which makes them less suitable for real-time and d…
Approximate k-space models and Deep Learning for fast photoacoustic reconstruction
Andreas Hauptmann, Ben Cox, Felix Lucka +4
We present a framework for accelerated iterative reconstructions using a fast and approximate forward model that is based on k-space methods for photoacoustic tomography. The appro…
Enhancing Compressed Sensing 4D Photoacoustic Tomography by Simultaneous Motion Estimation
Felix Lucka, Nam Huynh, Marta Betcke +4
A crucial limitation of current high-resolution 3D photoacoustic tomography (PAT) devices that employ sequential scanning is their long acquisition time. In previous work, we demon…
Choose your path wisely: gradient descent in a Bregman distance framework
Martin Benning, Marta M. Betcke, Matthias J. Ehrhardt +1
We propose an extension of a special form of gradient descent -- in the literature known as linearised Bregman iteration -- to a larger class of non-convex functions. We replace th…
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Andreas Hauptmann, Felix Lucka, Marta Betcke +6
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we…
Gradient descent in a generalised Bregman distance framework
Martin Benning, Marta M. Betcke, Matthias J. Ehrhardt +1
We discuss a special form of gradient descent that in the literature has become known as the so-called linearised Bregman iteration. The idea is to replace the classical (squared)…
Acoustic Wave Field Reconstruction from Compressed Measurements with Application in Photoacoustic Tomography
Marta M. Betcke, Ben T. Cox, Nam Huynh +3
We present a method for the recovery of compressively sensed acoustic fields using patterned, instead of point-by-point, detection. From a limited number of such compressed measure…
Nonzero-sum stochastic differential games with impulse controls: a verification theorem with applications
René Aïd, Matteo Basei, Giorgia Callegaro +2
We consider a general nonzero-sum impulse game with two players. The main mathematical contribution of the paper is a verification theorem which provides, under some regularity con…
On the Adjoint Operator in Photoacoustic Tomography
Simon R. Arridge, Marta M. Betcke, Ben T. Cox +2
Photoacoustic Tomography (PAT) is an emerging biomedical "imaging from coupled physics" technique, in which the image contrast is due to optical absorption, but the information is…
Multi-Contrast MRI Reconstruction with Structure-Guided Total Variation
Matthias J. Ehrhardt, Marta M. Betcke
Magnetic resonance imaging (MRI) is a versatile imaging technique that allows different contrasts depending on the acquisition parameters. Many clinical imaging studies acquire MRI…
A priorconditioned LSQR algorithm for linear ill-posed problems with edge-preserving regularization
Simon R. Arridge, Marta M. Betcke, Lauri Harhanen
This article presents a method for solving large-scale linear inverse problems regular- ized with a nonlinear, edge-preserving penalty term such as the total variation or Perona-Ma…