Johannes Leuschner

According to our database, Johannes Leuschner authored at least 16 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Image Reconstruction via Deep Image Prior Subspaces.

Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta

Trans. Mach. Learn. Res., 2024

Learning-Based Approaches for Reconstructions With Inexact Operators in nanoCT Applications.

Tom Lütjen, Fabian Schönfeld, Alice Oberacker, Johannes Leuschner, Maximilian Schmidt, Anne Wald, Tobias Kluth

IEEE Trans. Computational Imaging, 2024

2023

Deep learning for computed tomography reconstruction - learned methods, deep image prior and uncertainty estimation

Johannes Leuschner

PhD thesis, 2023

Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior.

Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

Trans. Mach. Learn. Res., 2023

Fast and Painless Image Reconstruction in Deep Image Prior Subspaces.

Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin

CoRR, 2023

SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction.

Marco Nittscher, Michael Falk Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass

Proceedings of the Medical Imaging with Deep Learning, 2023

2022

An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction.

Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maass, Bangti Jin

IEEE Trans. Computational Imaging, 2022

Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior.

Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato

CoRR, 2022

A Probabilistic Deep Image Prior for Computational Tomography.

Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin

CoRR, 2022

2021

Quantitative Comparison of Deep Learning-Based Image Reconstruction Methods for Low-Dose and Sparse-Angle CT Applications.

Johannes Leuschner, Maximilian Schmidt, Poulami Somanya Ganguly, Vladyslav Andriiashen, Sophia Bethany Coban, Alexander Denker, Dominik F. Bauer, Amir Hadjifaradji, Kees Joost Batenburg, Peter Maass, Maureen van Eijnatten

J. Imaging, 2021

Conditional Invertible Neural Networks for Medical Imaging.

Alexander Denker, Maximilian Schmidt, Johannes Leuschner, Peter Maass

J. Imaging, 2021

Is Deep Image Prior in Need of a Good Education?

Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin

CoRR, 2021

Training a Deep Neural Network via Policy Gradients for Blind Source Separation in Polyphonic Music Recordings.

Sören Schulze, Johannes Leuschner, Emily J. King

CoRR, 2021

2020

Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods.

Daniel Otero Baguer, Johannes Leuschner, Maximilian Schmidt

CoRR, 2020

2019

The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods.

Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß

CoRR, 2019

Supervised non-negative matrix factorization methods for MALDI imaging applications.

Johannes Leuschner, Maximilian Schmidt, Pascal Fernsel, Delf Lachmund, Tobias Boskamp, Peter Maass

Bioinform., 2019