Image Reconstruction in Dynamic Inverse Problems with Temporal Models

Andreas Hauptmann (), Ozan Öktem () and Carola Schönlieb ()
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Andreas Hauptmann: University of Oulu, Research Unit of Mathematical Sciences
Ozan Öktem: Uppsala University, Department of Information Technology, Division of Scientific Computing
Carola Schönlieb: University of Cambridge, Department of Applied Mathematics and Theoretical Physics

Chapter 48 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 1707-1737 from Springer

Abstract: Abstract This paper surveys variational approaches for image reconstruction in dynamic inverse problems. Emphasis is on variational methods that rely on parametrized temporal models. These are encoded here as diffeomorphic deformations with time-dependent parameters or as motion-constrained reconstructions where the motion model is given by a differential equation. The survey also includes recent developments in integrating deep learning for solving these computationally demanding variational methods. Examples are given for 2D dynamic tomography, but methods apply to general inverse problems.

Keywords: Image registration; Indirect registration; Inverse problems; Regularization; Tomography; Image reconstruction; Deep learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-030-98661-2_83

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