research
∙
09/11/2023
Anisotropic Diffusion Stencils: From Simple Derivations over Stability Estimates to ResNet Implementations
Anisotropic diffusion processes with a diffusion tensor are important in...
share
research
∙
03/17/2023
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting
With well-selected data, homogeneous diffusion inpainting can reconstruc...
research
∙
07/16/2022
CNN-based Euler's Elastica Inpainting with Deep Energy and Deep Image Prior
Euler's elastica constitute an appealing variational image inpainting mo...
research
∙
10/06/2021
Learning Sparse Masks for Diffusion-based Image Inpainting
Diffusion-based inpainting is a powerful tool for the reconstruction of ...
research
∙
08/31/2021
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
Partial differential equation (PDE) models and their associated variatio...
research
∙
07/30/2021
Connections between Numerical Algorithms for PDEs and Neural Networks
We investigate numerous structural connections between numerical algorit...
research
∙
03/29/2021
Translating Numerical Concepts for PDEs into Neural Architectures
We investigate what can be learned from translating numerical algorithms...
research
∙
03/25/2021
Multi-frame Super-resolution from Noisy Data
Obtaining high resolution images from low resolution data with clipped n...
research
∙
10/21/2020
Learning Integrodifferential Models for Image Denoising
We introduce an integrodifferential extension of the edge-enhancing anis...
research
∙
07/26/2020
PDE Evolutions for M-Smoothers in One, Two, and Three Dimensions
Local M-smoothers are interesting and important signal and image process...
research
∙
02/07/2020
Translating Diffusion, Wavelets, and Regularisation into Residual Networks
Convolutional neural networks (CNNs) often perform well, but their stabi...
research
∙
12/24/2019
Robustness of Brain Tumor Segmentation
We address the generalization behavior of deep neural networks in the co...
research
∙
12/12/2019
Variational Coupling Revisited: Simpler Models, Theoretical Connections, and Novel Applications
Variational models with coupling terms are becoming increasingly popular...
research
∙
10/21/2019
Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising
The rise of machine learning in image processing has created a gap betwe...
research
∙
10/05/2019
Object Segmentation Tracking from Generic Video Cues
We propose a light-weight variational framework for online tracking of o...
research
∙
03/08/2019
Stable Backward Diffusion Models that Minimise Convex Energies
Backward diffusion processes appear naturally in image enhancement and d...
research
∙
03/04/2019
Algorithms for Piecewise Constant Signal Approximations
We consider the problem of finding optimal piecewise constant approximat...
research
∙
12/18/2017
Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting
Exemplar-based inpainting is the process of reconstructing missing parts...
research
∙
06/15/2015