139 citations · 555 across the 26 of their papers we have counts for
5 papers · 1 filter
Score-Based Generative Models for PET Image Reconstruction
Imraj RD Singh, Alexander Denker, Riccardo Barbano +5
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography. However, their…
Parameter Identification by Deep Learning of a Material Model for Granular Media
Derick Nganyu Tanyu, Isabel Michel, Andreas Rademacher +2
Classical physical modelling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts of data (data-driven) are the two mos…
Invertible residual networks in the context of regularization theory for linear inverse problems
Clemens Arndt, Alexander Denker, Sören Dittmer +5
Learned inverse problem solvers exhibit remarkable performance in applications like image reconstruction tasks. These data-driven reconstruction methods often follow a two-step sch…
SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction
Marco Nittscher, Michael Lameter, Riccardo Barbano +3
The deep image prior (DIP) is a well-established unsupervised deep learning method for image reconstruction; yet it is far from being flawless. The DIP overfits to noise if not ear…
Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time
Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer +2
In this work, we propose a fast and accurate method to reconstruct activations of classification and semantic segmentation networks by stitching them with a GAN generator utilizing…