Scale Space and Partial Differential Equations Methods
Scale-covariant and Scale-invariant Gaussian Derivative Networks
Quantisation Scale-Spaces
Equivariant Deep Learning via Morphological and Linear Scale Space PDEs on the Space of Positions and Orientations
Nonlinear Spectral Processing of Shapes via Zero-homogeneous Flows
Total-Variation Mode Decomposition
Fast Morphological Dilation and Erosion for Grey Scale Images Using the Fourier Transform
Diffusion, Pre-Smoothing and Gradient Descent
Local Culprits of Shape Complexity
Extension of Mathematical Morphology in Riemannian Spaces
Flow, Motion and Registration
Multiscale Registration
Challenges for Optical Flow Estimates in Elastography
An Anisotropic Selection Scheme for Variational Optical Flow Methods with Order-Adaptive Regularisation
Low-rank Registration of Images Captured Under Unknown, Varying Lighting
Towards Efficient Time Stepping for Numerical Shape Correspondence
First Order Locally Orderless Registration
Optimization Theory and Methods in Imaging
First Order Geometric Multilevel Optimization For Discrete Tomography
Bregman Proximal Gradient Algorithms for Deep Matrix Factorization
Hessian Initialization Strategies for L-BFGS Solving Non-linear Inverse Problems
Inverse Scale Space Iterations for Non-Convex Variational Problems Using Functional Lifting
A Scaled and Adaptive FISTA Algorithm for Signal-dependent Sparse Image Super-resolution Problems
Convergence Properties of a Randomized Primal-Dual Algorithm with Applications to Parallel MRI
Machine Learning in Imaging
Wasserstein Generative Models for Patch-based Texture Synthesis
Sketched Learning for Image Denoising
Translating Numerical Concepts for PDEs into Neural Architectures
CLIP: Cheap Lipschitz Training of Neural Networks
Variational Models for Signal Processing with Graph Neural Networks
Synthetic Images as a Regularity Prior for Image Restoration Neural Networks
Geometric Deformation on Objects: Unsupervised Image Manipulation via Conjugation
Learning Local Regularization for Variational Image Restoration
Segmentation and Labelling
On the Correspondence between Replicator Dynamics and Assignment Flows
Learning Linear Assignment Flows for Image Labeling via Exponential Integration
On the Geometric Mechanics of Assignment Flows for Metric Data Labeling
A Deep Image Prior Learning Algorithm for Joint Selective Segmentation and Registration
Restoration, Reconstruction and Interpolation
Inpainting-based Video Compression in FullHD
Sparsity-aided Variational Mesh Restoration
Lossless PDE-based Compression of 3D Medical Images
Splines for Image Metamorphosis
Residual Whiteness Principle for Automatic Parameter Selection in `2-`2 Image Super-resolution Problems
Inverse Problems in Imaging
Total Deep Variation for Noisy Exit Wave Reconstruction in Transmission Electron Microscopy
GMM-based Simultaneous Reconstruction and Segmentation in X-ray CT application
Phase Retrieval via Polarization in Dynamical Sampling
Invertible Neural Networks versus MCMC for Posterior Reconstruction in Grazing Incidence X-Ray Fluorescence
Adversarially Learned Iterative Reconstruction for Imaging Inverse Problems
Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems
Multi-frame Super-resolution from Noisy Data.