121 citations · 327 across the 38 of their papers we have counts for
5 papers · 1 filter
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
Margaret Duff, Ivor J. A. Simpson, Matthias J. Ehrhardt +1
Objective: This paper investigates how generative models, trained on ground-truth images, can be used \changes{as} priors for inverse problems, penalizing reconstructions far from…
Regularising Inverse Problems with Generative Machine Learning Models
Margaret Duff, Neill D. F. Campbell, Matthias J. Ehrhardt
Deep neural network approaches to inverse imaging problems have produced impressive results in the last few years. In this paper, we consider the use of generative models in a vari…
Multi-modality imaging with structure-promoting regularisers
Matthias J. Ehrhardt
Imaging with multiple modalities or multiple channels is becoming increasingly important for our modern society. A key tool for understanding and early diagnosis of cancer and deme…
Robust Image Reconstruction with Misaligned Structural Information
Leon Bungert, Matthias J. Ehrhardt
Multi-modality (or multi-channel) imaging is becoming increasingly important and more widely available, e.g. hyperspectral imaging in remote sensing, spectral CT in material scienc…
Learning the Sampling Pattern for MRI
Ferdia Sherry, Martin Benning, Juan Carlos De los Reyes +5
The discovery of the theory of compressed sensing brought the realisation that many inverse problems can be solved even when measurements are "incomplete". This is particularly int…