6 citations · 6 across the 4 of their papers we have counts for
7 papers
On the unreasonable effectiveness of CNNs
Andreas Hauptmann, Jonas Adler
Deep learning methods using convolutional neural networks (CNN) have been successfully applied to virtually all imaging problems, and particularly in image reconstruction tasks wit…
Blind hierarchical deconvolution
Arttu Arjas, Lassi Roininen, Mikko J. Sillanpää +1
Deconvolution is a fundamental inverse problem in signal processing and the prototypical model for recovering a signal from its noisy measurement. Nevertheless, the majority of mod…
Image reconstruction in dynamic inverse problems with temporal models
Andreas Hauptmann, Ozan Öktem, Carola Schönlieb
The paper surveys variational approaches for image reconstruction in dynamic inverse problems. Emphasis is on methods that rely on parametrised temporal models. These are here enco…
Joint Reconstruction and Low-Rank Decomposition for Dynamic Inverse Problems
Simon Arridge, Pascal Fernsel, Andreas Hauptmann
A primary interest in dynamic inverse problems is to identify the underlying temporal behaviour of the system from outside measurements. In this work we consider the case, where th…
Towards accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in 3D
Ciaran Bench, Andreas Hauptmann, Ben Cox
Significance: 2D fully convolutional neural networks have been shown capable of producing maps of sO from 2D simulated images of simple tissue models. However, their potential…
Rapid Whole-Heart CMR with Single Volume Super-resolution
Jennifer A. Steeden, Michael Quail, Alexander Gotschy +4
Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have…