Inverse Problems

Solving Inverse Problems With Deep Neural Networks - Robustness Included?
In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems. Before such approaches …
Martin Genzel, Jan Macdonald, Maximilian März
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Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images
This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and …
Theophil Trippe, Martin Genzel, Jan Macdonald, Maximilian März
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The Reliability of Deep Learning for Signal and Image Processing: Interpretability, Robustness, and Accuracy
This thesis investigates several aspects of using data-driven methods for image and signal processing tasks, particularly those aspects …
Jan Macdonald
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Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
This work is concerned with the following fundamental question in scientific machine learning: Can deep-learning-based methods solve …
Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März
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Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning
Long Oral & Poster: Presentation of results from our paper.
Jul 21, 2022 4:00 PM — 4:20 PM Baltimore Convention Center, Baltimore, USA
Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März
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Solving Inverse Problems With Deep Neural Networks - Robustness Included?
Invited Talk: Presentation of results from our paper.
May 26, 2022 9:50 AM — 7:30 PM Paradise Bay Resort, Mellieha, Malta
Martin Genzel, Jan Macdonald, Maximilian März
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Learning to Invert Defocus Blur: A Data-Driven Approach to the Helsinki Deblur Challenge
Invited Talk: Presentation of our 1st place winning submission to the Helsinki Deblur Challenge.
Dec 15, 2021 9:50 AM — 10:50 AM Tampere University (Hybrid Virtual Conference)
Theophil Trippe, Martin Genzel, Jan Macdonald, Maximilian März
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Near-Exact Recovery for Sparse-View CT via Data-Driven Methods
Poster: Presentation of a detailed analysis of our 1st place winning AAPM DL-Sparse-View CT challenge submission.
Dec 13, 2021 12:45 PM — 2:00 PM Virtual Conference
Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März
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Near-Exact Recovery for Sparse-View CT via Data-Driven Methods
This work presents an empirical study on the design and training of iterative neural networks for image reconstruction from tomographic …
Martin Genzel, Ingo Gühring, Jan Macdonald, Maximilian März
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Detecting Failure Modes in Image Reconstructions with Interval Neural Network Uncertainty

Purpose The quantitative detection of failure modes is important for making deep neural networks reliable and usable at scale. We …

Luis Oala, Cosmas Heiß, Jan Macdonald, Maximilian März, Gitta Kutyniok, Wojciech Samek
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