This paper surveys variational approaches for image reconstruction in dynamic inverse
problems. Emphasis is on variational methods that rely on parametrized temporal models …

Data-driven deep learning approaches to image registration can be less accurate than
conventional iterative approaches, especially when training data is limited. To address this …

In this work, we investigate image registration in a variational framework and focus on
regularization generality and solver efficiency. We first propose a variational model …

The paper considers the problem of performing a post-processing task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed inverse …

The classic regularization theory for solving inverse problems is built on the assumption that
the forward operator perfectly represents the underlying physical model of the data …

Fast and accurate MRI reconstruction is a key concern in modern clinical practice. Recently,
numerous Deep-Learning methods have been proposed for MRI reconstruction, however …

The risk assessment of carotid plaque is strongly related to the plaque echo status in
ultrasound. However, the echo classification of carotid plaques based on ultrasound …

This paper presents a novel method for the reconstruction of high-resolution temporal
images in dynamic tomographic imaging, particularly for discrete objects with smooth …

Diffeomorphic registration is a widely used technique for finding a smooth and invertible
transformation between two coordinate systems, which are measured using template and …

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