Inverse Problems in Imaging
Explicit Diffusion of Gaussian Mixture Model Based Image Priors
Efficient Neural Generation of 4K Masks for Homogeneous Diffusion Inpainting
Theoretical Foundations for Pseudo-Inversion of Nonlinear Operators
A Frame Decomposition of the Funk-Radon Transform
Prony-Based Super-Resolution Phase Retrieval of Sparse, Multidimensional Signals
Limited Electrodes Models in Electrical Impedance Tomography Reconstruction
On Trainable Multiplicative Noise Removal Models
Surface Reconstruction from 2D Noisy Point Cloud Data using Directional G-norm
Regularized Material Decomposition for K-Edge Separation in Hyperspectral Computed Tomography
Quaternary Image Decomposition with Cross-Correlation-Based Multi-Parameter Selection
Machine and Deep Learning in Imaging
EmNeF: Neural Fields for Embedded Variational Problems in Imaging
GenHarris-ResNet: A Rotation Invariant Neural Network Based on Elementary Symmetric Polynomials
Compressive Learning of Deep Regularization for Denoising
Graph Laplacian and Neural Networks for Inverse Problems in Imaging: graphLaNet
Learning Posterior Distributions in Underdetermined Inverse Problems
Proximal Residual Flows for Bayesian Inverse Problems
A Model Is Worth Tens of Thousands of Examples
Resolution-Invariant Image Classification Based on Fourier Neural Operators
Graph Laplacian for Semi-Supervised Learning
A Geometrically Aware Auto-Encoder for Multi-Texture Synthesis
Fast Marching Energy CNN
Deep Accurate Solver for the Geodesic Problem
Deep Image Prior Regularized by Coupled Total Variation for Image Colorization
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras
Latent-Space Disentanglement with Untrained Generator Networks for the Isolation of Different Motion Types in Video Data
Natural Numerical Networks on Directed Graphs in Satellite Image Classification
Piece-Wise Constant Image Segmentation with a Deep Image PriorApproach
On the Inclusion of Topological Requirements in CNNs for Semantic Segmentation Applied to Radiotherapy
Optimization for Imaging: Theory and Methods
A Relaxed Proximal Gradient Descent Algorithm for Convergent Plug-and-Play with Proximal Denoiser
Off-the-Grid Charge Algorithm for Curve Reconstruction in Inverse Problems
Convergence Guarantees of Overparametrized Wide Deep Inverse Prior
On the Remarkable Efficiency of SMART
Wasserstein Gradient Flows of the Discrepancy with Distance Kernel on the Line
A Quasi-Newton Primal-Dual Algorithm with Line Search
Stochastic Gradient Descent for Linear Inverse Problems in Variable Exponent Lebesgue Spaces
An Efficient Line Search for Sparse Reconstruction
Learned Discretization Schemes for the Second-Order Total Generalized Variation
Fluctuation-Based Deconvolution in Fluorescence Microscopy Using Plug-and-Play Denoisers
Segmenting MR Images Through Texture Extraction and Multiplicative Components Optimization
Scale Space, PDEs, Flow, Motion and Registration
Geodesic Tracking of Retinal Vascular Trees with Optical and TV-Flow Enhancement in SE(2)
Geometric Adaptations of PDE-G-CNNs
The Variational Approach to the Flow of Sobolev-Diffeomorphisms Model
Image Comparison and Scaling via Nonlinear Elasticity
Learning Differential Invariants of Planar Curves
Diffusion-Shock Inpainting
Generalised Scale-Space Properties for Probabilistic Diffusion Models
Gromov-Wasserstein Transfer Operators
Optimal Transport Between GMM for Multiscale Texture Synthesis
Asymptotic Result for a Decoupled Nonlinear Elasticity-Based Multiscale Registration Model
Image Blending with Osmosis
-Pixels for Hierarchical Analysis of Digital Objects
Hypergraph p-Laplacians, Scale Spaces, and Information Flow in Networks
On Photometric Stereo in the Presence of a Refractive Interface
Multi-View Normal Estimation Application to Slanted Plane-Sweeping
Partial Shape Similarity by Multi-Metric Hamiltonian Spectra Matching
Modeling Large-Scale Joint Distributions and Inference by Randomized Assignment
Quantum State Assignment Flows.