Combined Background Field Removal and Reconstruction for Quantitative Susceptibility Mapping. - Maximilian März; Lars Ruthotto (Bildverarbeitung für die Medizin, 2016)

Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting. - Jackie Ma; Maximilian März; Stephanie Funk; Jeanette Schulz-Menger; Gitta Kutyniok; Tobias Schaeffter; Christoph Kolbitsch (CoRR, 2017)

$ ell^1$-Analysis Minimization and Generalized (Co-)Sparsity: When Does Recovery Succeed? - Martin Genzel; Gitta Kutyniok; Maximilian März (CoRR, 2017)

Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography. - Tatiana A. Bubba; Gitta Kutyniok; Matti Lassas; Maximilian März; Wojciech Samek; Samuli Siltanen; Vignesh Srinivasan (CoRR, 2018)

Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery. - Martin Genzel; Maximilian März; Robert Seidel (CoRR, 2020)

Interval Neural Networks: Uncertainty Scores. - Luis Oala; Cosmas Heiss; Jan MacDonald; Maximilian März; Wojciech Samek; Gitta Kutyniok (CoRR, 2020)

Compressed Sensing with 1D Total Variation: Breaking Sample Complexity Barriers via Non-Uniform Recovery (iTWIST'20). - Martin Genzel; Maximilian März; Robert Seidel (CoRR, 2020)

Correcting the Side Effects of ADC Filtering in MR Image Reconstruction. - Carole Lazarus; Maximilian März; Pierre Weiss (J. Math. Imaging Vis., 2020)

Interval Neural Networks as Instability Detectors for Image Reconstructions. - Jan MacDonald; Maximilian März; Luis Oala; Wojciech Samek (Bildverarbeitung für die Medizin, 2021)

Detecting failure modes in image reconstructions with interval neural network uncertainty. - Luis Oala; Cosmas Heiß; Jan MacDonald; Maximilian März; Gitta Kutyniok; Wojciech Samek (Int. J. Comput. Assist. Radiol. Surg., 2021)

AAPM DL-Sparse-View CT Challenge Submission Report: Designing an Iterative Network for Fanbeam-CT with Unknown Geometry. - Martin Genzel; Jan MacDonald; Maximilian März (CoRR, 2021)

Solving underdetermined inverse problems: from advanced sparsity models to deep learning. - Maximilian März (2021)

Near-Exact Recovery for Tomographic Inverse Problems via Deep Learning. - Martin Genzel; Ingo Gühring; Jan MacDonald; Maximilian März (ICML, 2022)

Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images. - Theophil Trippe; Martin Genzel; Jan MacDonald; Maximilian März (CoRR, 2022)

Solving Inverse Problems With Deep Neural Networks - Robustness Included? - Martin Genzel; Jan MacDonald; Maximilian März (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Sampling Rates for ℓ 1-Synthesis. - Maximilian März; Claire Boyer; Jonas Kahn; Pierre Weiss (Found. Comput. Math., 2023)