6 citations · 13 across the 23 of their papers we have counts for
8 papers · 1 filter
MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
Arya Bangun, Zhuo Cao, Alessio Quercia +2
Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, t…
Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction
Qin Wang, Kai Krajsek, Hanno Scharr
Augmentation-based self-supervised learning methods have shown remarkable success in self-supervised visual representation learning, excelling in learning invariant features but of…
Retrieval of sun-induced plant fluorescence in the O-A absorption band from DESIS imagery
Jim Buffat, Miguel Pato, Kevin Alonso +7
We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation () to high-quality airborne estimates of sun-induc…
EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
Nils Friederich, Angelo Jovin Yamachui Sitcheu, Annika Nassal +12
Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data…
Untrained Perceptual Loss for image denoising of line-like structures in MR images
Elisabeth Pfaehler, Daniel Pflugfelder, Hanno Scharr
In the acquisition of Magnetic Resonance (MR) images shorter scan times lead to higher image noise. Therefore, automatic image denoising using deep learning methods is of high inte…
Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
J. Seiffarth, L. Blöbaum, R. D. Paul +6
Tracking the development of living cells in live-cell time-lapses reveals crucial insights into single-cell behavior and presents tremendous potential for biomedical and biotechnol…
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation
Richard D. Paul, Alessio Quercia, Vincent Fortuin +2
State-of-the-art computer vision tasks, like monocular depth estimation (MDE), rely heavily on large, modern Transformer-based architectures. However, their application in safety-c…
Robust Approximate Characterization of Single-Cell Heterogeneity in Microbial Growth
Richard D. Paul, Johannes Seiffarth, Hanno Scharr +1
Live-cell microscopy allows to go beyond measuring average features of cellular populations to observe, quantify and explain biological heterogeneity. Deep Learning-based instance…