755 citations · 2k across the 16 of their papers we have counts for
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Learning to solve inverse problems using Wasserstein loss
Jonas Adler, Axel Ringh, Ozan Öktem +1
We propose using the Wasserstein loss for training in inverse problems. In particular, we consider a learned primal-dual reconstruction scheme for ill-posed inverse problems using…
Model based learning for accelerated, limited-view 3D photoacoustic tomography
Andreas Hauptmann, Felix Lucka, Marta Betcke +6
Recent advances in deep learning for tomographic reconstructions have shown great potential to create accurate and high quality images with a considerable speed-up. In this work we…
Learned Primal-dual Reconstruction
Jonas Adler, Ozan Öktem
We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolli…
Solving ill-posed inverse problems using iterative deep neural networks
Jonas Adler, Ozan Öktem
We propose a partially learned approach for the solution of ill posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regu…