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 -- α-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.