755 citations · 2k across the 16 of their papers we have counts for
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Deep Bayesian Inversion
Jonas Adler, Ozan Öktem
Characterizing statistical properties of solutions of inverse problems is essential for decision making. Bayesian inversion offers a tractable framework for this purpose, but curre…
Task adapted reconstruction for inverse problems
Jonas Adler, Sebastian Lunz, Olivier Verdier +2
The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect…
Deep Learning Framework for Digital Breast Tomosynthesis Reconstruction
Nikita Moriakov, Koen Michielsen, Jonas Adler +3
Digital breast tomosynthesis is rapidly replacing digital mammography as the basic x-ray technique for evaluation of the breasts. However, the sparse sampling and limited angular r…
Data-driven nonsmooth optimization
Sebastian Banert, Axel Ringh, Jonas Adler +2
In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function. The key idea is to first specify a class of optimizati…
Banach Wasserstein GAN
Jonas Adler, Sebastian Lunz
Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on…
A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images
Awais Ashfaq, Jonas Adler
CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image-domain correction algorithms have been proposed in the literature that use patient-specific…