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3 papers
Data-proximal null-space networks for inverse problems
Simon Göppel, Jürgen Frikel, Markus Haltmeier
Inverse problems are inherently ill-posed and therefore require regularization techniques to achieve a stable solution. While traditional variational methods have well-established…
Translation invariant diagonal frame decomposition for the Radon transform
Simon Göppel, Markus Haltmeier, Jürgen Frikel
In this article, we address the challenge of solving the ill-posed reconstruction problem in computed tomography using a translation invariant diagonal frame decomposition (TI-DFD)…
Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction
Christoph Angermann, Simon Göppel, Markus Haltmeier
Reconstructing an image from noisy and incomplete measurements is a central task in several image processing applications. In recent years, state-of-the-art reconstruction methods…