Various imaging modalities allow for time-dependent image reconstructions from
measurements where its acquisition also has a time-dependent nature. Magnetic particle …

Posterior sampling has been shown to be a powerful Bayesian approach for solving imaging
inverse problems. The recent plug-and-play unadjusted Langevin algorithm (PnP-ULA) has …

Inverse problems are in many cases solved with optimization techniques. When the
underlying model is linear, first-order gradient methods are usually sufficient. With nonlinear …

Various imaging modalities allow for time-dependent image reconstructions from
measurements where its acquisition also has a time-dependent nature. Magnetic particle …

Imaging problems such as the one in nanoCT require the solution of an inverse problem,
where it is often taken for granted that the forward operator, ie, the underlying physical …

This is the documentation for the Spatio-TEmporal Motor-POwered (STEMPO) phantom for
dynamic X-ray tomography. Different measurements designed for testing dynamic …

Separating and eliminating periodic disturbances from measured signals are a key problem
to obtain original responses used for further system identification and evaluation. Actual …

Advances in learning-based methods are revolutionizing several fields in applied
mathematics, including inverse problems, resulting in a major paradigm shift towards data …

The recent development of scintillation crystals combined with γ-rays sources opens the way
to an imaging concept based on Compton scattering, namely Compton scattering …

Efficient representations of multivariate functions are critical for the design of state-of-the-art
methods of data restoration and image reconstruction. In this work, we consider the …