44 citations · 56 across the 10 of their papers we have counts for
6 papers · 1 filter
Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction
Riccardo Barbano, Željko Kereta, Chen Zhang +3
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 o…
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…
Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models
Callum M. Macdonald, Simon Arridge, Samuel Powell
Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally…
Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent
Riccardo Barbano, Chen Zhang, Simon Arridge +1
Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e.g., deep neural networks. These techniques have demonstrated state-of-the-art…
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…
On Learned Operator Correction in Inverse Problems
Sebastian Lunz, Andreas Hauptmann, Tanja Tarvainen +2
We discuss the possibility to learn a data-driven explicit model correction for inverse problems and whether such a model correction can be used within a variational framework to o…