87 citations · 182 across the 19 of their papers we have counts for
4 papers · 1 filter
DREAM: Deep-Reparametrization of Adaptive Regularization Maps for Fast Zero-Shot Self-Supervised Learning
Thanh Trung Vu, Ander Biguri, Christoph Kolbitsch +3
Adaptive regularization is an effective means of improving the flexibility of classical variational reconstruction methods while retaining their interpretability and mathematical s…
Machine Learning for Quantitative MR Image Reconstruction
Andreas Kofler, Felix Frederik Zimmermann, Kostas Papafitsoros
In the last years, the design of image reconstruction methods in the field of quantitative Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when dealing w…
Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based o…
Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variati…