Compressed sensing for the sparse Radon transform

Giovanni S. Alberti, Alessandro Felisi, Matteo Santacesaria, S. Ivan Trapasso

Regularization for learning from unlabeled data using related labeled data

Werner Zellinger, Sergei V. Pereverzyev

Random tree Besov priors for detail detection

Hanne Kekkonen, Matti Lassas, Samuli Siltanen

SGD for statistical inverse problems

Abhishake Abhishake

Convex regularization in statistical inverse learning problems

Tapio Helin

An off-the-grid approach to multi-compartment magnetic fingerprinting

Clarice Poon

How many Neurons do we need? A refined Analysis.

Mike Nguyen, Nicole Mücke

Reconstructing spatio-temporal, sparse tomographic data using cylindrical shearlets

Tatiana Alessandra Bubba

Fractal priors for imaging using random wavelet trees

Samuli Siltanen

Sampling from a posterior with Besov prior

Andreas Horst, Babak Maboudi Afkham, Yiqiu Dong, Jakob Lemvig

Regularizing Inverse Problems through Translation Invariant Diagonal Frame Decompositions

Jürgen Frikel

Regularized, pretrained and subspace-restricted Deep Image Prior for CT reconstruction

Riccardo Barbano, Javier Antorán, Johannes Leuschner, Bangti Jin, José Miguel Hernández-Lobato, Zeljko Kereta, Daniel Otero Baguer, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß

Monitoring of hemorrhagic stroke using Electrical Impedance Tomography

Ville Kolehmainen

Edge-preserving inversion with $\alpha$-stable priors

Jarkko Suuronen, Tomás Soto, Neil Chada, Lassi Roininen

Optimal learning of high-dimensional classification problems using deep neural networks

Felix Voigtlaender

Continuum limit for lattice Hamiltonians

Hiroshi Isozaki

Quantum computing algorithms for inverse problems on graphs

Joonas Ilmavirta, Matti Lassas, Jinpeng Lu, Lauri Oksanen, Lauri Ylinen

Inverse problems for the graph Laplacian

Emilia Blåsten, Hiroshi Isozaki, Matti Lassas, Jinpeng Lu

Inverse problems on manifolds via graph-based semi-supervised learning

Daniel Sanz-Alonso, Ruiyi Yang

Deep Invertible Approximation of Topologically Rich Maps between Manifolds

Michael Puthawala, Matti Lassas, Ivan Dokmanic, Pekka Pankka, Maarten de Hoop

Some inverse problems on graphs with internal functionals

Fernando Guevara Vasquez, Guang Yang

Imaging water supply pipes using pressure waves

Emilia Lavie Kyllikki Blåsten, Fedi Zouari, Moez Louati, Mohamed S. Ghidaoui

Recontructing Interactions from Dynamics

Ivan Dokmanic, Liming Pan, Cheng Shi

Learned Solvers for Forward and Backward Image Flow Schemes

Simon Robert Arridge, Andreas Selmar Hauptmann, Giuseppe di Sciacca, Wiryawan Mehanda

Resolution of reconstruction from discrete Radon transform data

Alexander Katsevich

Deep Learning for Reconstruction in Nano CT

Alice Oberacker, Anne Wald, Bernadette Hahn-Rigaud, Tobias Kluth, Johannes Leuschner, Maximilian Schmidt, Thomas Schuster

Learned post-processing approaches for nano-CT reconstruction

Tom Lütjen, Fabian Schönfeld, Alice Oberacker, Maximilian Schmidt, Johannes Leuschner, Tobias Kluth, Anne Wald

X-ray phase and dark-field retrieval from propagation-based images, via the Fokker-Planck Equation

Kaye Susannah Morgan, Thomas Leatham, Mario Beltran, Jannis Ahlers, Samantha Alloo, Marcus Kitchen, Konstantin Pavlov, David Paganin

Multi-stage Deep Learning Artifact Reduction for Computed Tomography

Jiayang Shi, Daan Pelt, Joost Batenburg

Deep learning for phase retrieval from Fresnel diffraction patterns

Max Langer, Kannara Mom, Bruno Sixou

Time resolved and multi-resolution tomographic reconstruction strategies in practice.

Rajmund Mokso, Viktor Nikitin

Tomographic Reconstruction in X-ray Near-field Diffractive Imaging: from Laboratory $\mu$CT to Synchrotron Nano-Imaging

Tim Salditt