Search Results for author: Maximilian März

Found 8 papers, 6 papers with code

Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images

1 code implementation18 Nov 2022 Theophil Trippe, Martin Genzel, Jan Macdonald, Maximilian März

This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data.

Deblurring Image Deblurring +2

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning

1 code implementation14 Jun 2022 Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve noise-free inverse problems to near-perfect accuracy?

Computed Tomography (CT)

Near-Exact Recovery for Sparse-View CT via Data-Driven Methods

no code implementations NeurIPS Workshop Deep_Invers 2021 Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März

This work presents an empirical study on the design and training of iterative neural networks for image reconstruction from tomographic measurements with unknown geometry.

Image Reconstruction

AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry

1 code implementation1 Jun 2021 Martin Genzel, Jan Macdonald, Maximilian März

This report is dedicated to a short motivation and description of our contribution to the AAPM DL-Sparse-View CT Challenge (team name: "robust-and-stable").

Solving Inverse Problems With Deep Neural Networks -- Robustness Included?

1 code implementation9 Nov 2020 Martin Genzel, Jan Macdonald, Maximilian März

In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems.

Image Reconstruction

Interval Neural Networks as Instability Detectors for Image Reconstructions

1 code implementation27 Mar 2020 Jan Macdonald, Maximilian März, Luis Oala, Wojciech Samek

This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks.

Image Reconstruction Uncertainty Quantification

Interval Neural Networks: Uncertainty Scores

1 code implementation25 Mar 2020 Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Wojciech Samek, Gitta Kutyniok

We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.

Image Reconstruction Uncertainty Quantification

Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting

no code implementations1 May 2017 Jackie Ma, Maximilian März, Stephanie Funk, Jeanette Schulz-Menger, Gitta Kutyniok, Tobias Schaeffter, Christoph Kolbitsch

High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times.

Anatomy Image Reconstruction

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