6 citations · 6 across the 3 of their papers we have counts for
14 papers
Equivariant neural networks for inverse problems
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann +3
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of…
Synergistic Multi-spectral CT Reconstruction with Directional Total Variation
Evelyn Cueva, Alexander Meaney, Samuli Siltanen +1
This work considers synergistic multi-spectral CT reconstruction where information from all available energy channels is combined to improve the reconstruction of each individual c…
Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI
Eric B. Gutierrez, Claire Delplancke, Matthias J. Ehrhardt
The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale optimization problems.…
Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
Matthias J. Ehrhardt, Lindon Roberts
Many machine learning solutions are framed as optimization problems which rely on good hyperparameters. Algorithms for tuning these hyperparameters usually assume access to exact s…
A temporal multiscale approach for MR Fingerprinting
Samuel Cortinhas, Mohammad Golbabaee, Matthias J. Ehrhardt
Quantitative MRI (qMRI) is becoming increasingly important for research and clinical applications, however, state-of-the-art reconstruction methods for qMRI are computationally pro…
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…