1 code implementation • 28 Mar 2023 • Marco Nittscher, Michael Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless.
1 code implementation • 20 Feb 2023 • Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Željko Kereta
Deep learning has been widely used for solving image reconstruction tasks but its deployability has been held back due to the shortage of high-quality training data.
1 code implementation • 11 Jul 2022 • Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction.
2 code implementations • 28 Feb 2022 • Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment.
3 code implementations • 23 Nov 2021 • Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks.
2 code implementations • 26 Oct 2021 • Alexander Denker, Maximilian Schmidt, Johannes Leuschner, Peter Maass
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems.
no code implementations • 9 Jul 2021 • Sören Schulze, Johannes Leuschner, Emily J. King
We propose a method for the blind separation of sounds of musical instruments in audio signals.
1 code implementation • 10 Mar 2020 • Daniel Otero Baguer, Johannes Leuschner, Maximilian Schmidt
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime.
1 code implementation • 1 Oct 2019 • Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field.