179 citations · 322 across the 10 of their papers we have counts for
11 papers
Learning-based approaches for reconstructions with inexact operators in nanoCT applications
Tom Lütjen, Fabian Schönfeld, Alice Oberacker +4
Imaging problems such as the one in nanoCT require the solution of an inverse problem, where it is often taken for granted that the forward operator, i.e., the underlying physical…
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
Marco Nittscher, Michael Lameter, Riccardo Barbano +3
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not ear…
Image Reconstruction via Deep Image Prior Subspaces
Riccardo Barbano, Javier Antorán, Johannes Leuschner +3
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. Unsupervised l…
Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
Riccardo Barbano, Johannes Leuschner, Javier Antorán +2
We investigate adaptive design based on a single sparse pilot scan for generating effective scanning strategies for computed tomography reconstruction. We propose a novel approach…
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
Javier Antorán, Riccardo Barbano, Johannes Leuschner +2
Existing deep-learning based tomographic image reconstruction methods do not provide accurate estimates of reconstruction uncertainty, hindering their real-world deployment. This p…
An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt +4
Deep image prior (DIP) was recently introduced as an effective unsupervised approach for image restoration tasks. DIP represents the image to be recovered as the output of a deep c…
Conditional Invertible Neural Networks for Medical Imaging
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +1
Over the last years, deep learning methods have become an increasingly popular choice to solve tasks from the field of inverse problems. Many of these new data-driven methods have…
Blind Source Separation in Polyphonic Music Recordings Using Deep Neural Networks Trained via Policy Gradients
Sören Schulze, Johannes Leuschner, Emily J. King
We propose a method for the blind separation of sounds of musical instruments in audio signals. We describe the individual tones via a parametric model, training a dictionary to ca…
Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction
Alexander Denker, Maximilian Schmidt, Johannes Leuschner +2
Image reconstruction from computed tomography (CT) measurement is a challenging statistical inverse problem since a high-dimensional conditional distribution needs to be estimated.…
Computed Tomography Reconstruction Using Deep Image Prior and Learned Reconstruction Methods
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. As motivation, we review some of the exist…
The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
Johannes Leuschner, Maximilian Schmidt, Daniel Otero Baguer +1
Deep Learning approaches for solving Inverse Problems in imaging have become very effective and are demonstrated to be quite competitive in the field. Comparing these approaches is…