A Variational Model for Joint Motion Estimation and Image Reconstruction
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Abstract (arXiv)
The aim of this paper is to derive and analyze a variational model for the joint estimation of motion and reconstruction of image sequences, which is based on a time-continuous Eulerian motion model. The model can be set up in terms of the continuity equation or the brightness constancy equation. The analysis in this paper focuses on the latter for robust motion estimation on sequences of two-dimensional images. We rigorously prove the existence of a minimizer in a suitable function space setting. Moreover, we discuss the numerical solution of the model based on primal-dual algorithms and investigate several examples. Finally, the benefits of our model compared to existing techniques, such as sequential image reconstruction and motion estimation, are shown.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jul 2016.
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Citing papers per year: 2023: 2; 2024: 2; 2025: 2.
6 papers.
- 2025Diff-Unfolding: A Model-Based Score Learning Framework for Inverse Problemscited by 3
- 2023Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNscited by 1
- 2024Enhancing Dynamic CT Image Reconstruction with Neural Fields and Optical Flowcited by 1
- 2025Joint Manifold Learning and Optimal Transport for Dynamic Imagingcited by 1
- 2023Single-shot Tomography of Discrete Dynamic Objectscited by 0
- 2024DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescalecited by 0
- 2025Joint Manifold Learning and Optimal Transport for Dynamic Imaging17 May 2025
- 2025Diff-Unfolding: A Model-Based Score Learning Framework for Inverse Problems16 May 2025
- 2024DYRECT Computed Tomography: DYnamic Reconstruction of Events on a Continuous Timescale15 Nov 2024
- 2024Enhancing Dynamic CT Image Reconstruction with Neural Fields and Optical Flow3 Jun 2024
- 2023Single-shot Tomography of Discrete Dynamic Objects9 Nov 2023
- 2023Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs30 Mar 2023