# Image reconstruction in dynamic inverse problems with temporal models

arXiv: https://arxiv.org/abs/2007.10238 · HTML: https://mlsanity.com/paper/2007.10238/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Sequential Posterior Sampling with Diffusion Models](https://mlsanity.com/paper/2409.05399/) (2024)
- [Enhancing Dynamic CT Image Reconstruction with Neural Fields and Optical Flow](https://mlsanity.com/paper/2406.01299/) (2024)
- [Joint Manifold Learning and Optimal Transport for Dynamic Imaging](https://mlsanity.com/paper/2505.11913/) (2025)
- [A tomographic workflow to enable deep learning for X-ray based foreign object detection](https://mlsanity.com/paper/2201.12184/) (2022)
- [Single-shot Tomography of Discrete Dynamic Objects](https://mlsanity.com/paper/2311.05269/) (2023)

## Abstract (arXiv)

The paper surveys variational approaches for image reconstruction in dynamic inverse problems. Emphasis is on methods that rely on parametrised temporal models. These are here encoded as diffeomorphic deformations with time dependent parameters, or as motion constrained reconstruction where the motion model is given by a partial differential equation. The survey also includes recent development 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.