2 citations · 2 across the 2 of their papers we have counts for
3 papers
Improving Deep Learning for Accelerated MRI With Data Filtering
Kang Lin, Anselm Krainovic, Kun Wang +1
Deep neural networks achieve state-of-the-art results for accelerated MRI reconstruction. Most research on deep learning based imaging focuses on improving neural network architect…
Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation
Anselm Krainovic, Stefan Ruschke, Reinhard Heckel
Deep learning-based 3D imaging, in particular magnetic resonance imaging (MRI), is challenging because of limited availability of 3D training data. Therefore, 2D diffusion models t…
Learning Provably Robust Estimators for Inverse Problems via Jittering
Anselm Krainovic, Mahdi Soltanolkotabi, Reinhard Heckel
Deep neural networks provide excellent performance for inverse problems such as denoising. However, neural networks can be sensitive to adversarial or worst-case perturbations. Thi…