4 citations · 12 across the 14 of their papers we have counts for
6 papers · 1 filter
Learning Sparse Masks for Diffusion-based Image Inpainting
Tobias Alt, Pascal Peter, Joachim Weickert
Diffusion-based inpainting is a powerful tool for the reconstruction of images from sparse data. Its quality strongly depends on the choice of known data. Optimising their spatial…
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
Tobias Alt, Karl Schrader, Joachim Weickert +2
Partial differential equation (PDE) models and their associated variational energy formulations are often rotationally invariant by design. This ensures that a rotation of the inpu…
Connections between Numerical Algorithms for PDEs and Neural Networks
Tobias Alt, Karl Schrader, Matthias Augustin +2
We investigate numerous structural connections between numerical algorithms for partial differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set…
Quantisation Scale-Spaces
Pascal Peter
Recently, sparsification scale-spaces have been obtained as a sequence of inpainted images by gradually removing known image data. Thus, these scale-spaces rely on spatial sparsity…
Translating Numerical Concepts for PDEs into Neural Architectures
Tobias Alt, Pascal Peter, Joachim Weickert +1
We investigate what can be learned from translating numerical algorithms into neural networks. On the numerical side, we consider explicit, accelerated explicit, and implicit schem…
JPEG Meets PDE-based Image Compression
Sarah Andris, Joachim Weickert, Tobias Alt +1
Inpainting-based image compression is emerging as a promising competitor to transform-based compression techniques. Its key idea is to reconstruct image information from only few k…