# Dynamic super-resolution in particle tracking problems

arXiv: https://arxiv.org/abs/2204.04092 · HTML: https://mlsanity.com/paper/2204.04092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 54 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 6 of 6)

- [Exact Support Recovery for Sparse Spikes Deconvolution](https://mlsanity.com/paper/1306.6909/) (2013)
- [Algorithmic Foundations for the Diffraction Limit](https://mlsanity.com/paper/2004.07659/) (2020)
- [Super-Resolution of Positive Sources: the Discrete Setup](https://mlsanity.com/paper/1504.00717/) (2015)
- [Mathematical Theory of Computational Resolution Limit in Multi-dimensions](https://mlsanity.com/paper/2103.11632/) (2021)
- [Super-Resolution of Positive Sources on an Arbitrarily Fine Grid](https://mlsanity.com/paper/2005.06756/) (2020)
- [A measurement decoupling based fast algorithm for super-resolving point sources with multi-cluster structure](https://mlsanity.com/paper/2204.00469/) (2022)

## Abstract (arXiv)

Particle tracking in biological imaging is concerned with reconstructing the trajectories, locations, or velocities of the targeting particles. The standard approach of particle tracking consists of two steps: first reconstructing statically the source locations in each time step, and second applying tracking techniques to obtain the trajectories and velocities. In contrast, the dynamic reconstruction seeks to simultaneously recover the source locations and velocities from all frames, which enjoys certain advantages. In this paper, we provide a rigorous mathematical analysis for the resolution limit of reconstructing source number, locations, and velocities by general dynamical reconstruction in particle tracking problems, by which we demonstrate the possibility of achieving super-resolution for the dynamic reconstruction. We show that when the location-velocity pairs of the particles are separated beyond certain distances (the resolution limits), the number of particles and the location-velocity pair can be stably recovered. The resolution limits are related to the cut-off frequency of the imaging system, signal-to-noise ratio, and the sparsity of the source. By these estimates, we also derive a stability result for a sparsity-promoting dynamic reconstruction. In addition, we further show that the reconstruction of velocities has a better resolution limit which improves constantly as the particles moving. This result is derived by an observation that the inherent cut-off frequency for the velocity recovery can be viewed as the total observation time multiplies the cut-off frequency of the imaging system, which may lead to a better resolution limit as compared to the one for each diffraction-limited frame. It is anticipated that this observation can inspire new reconstruction algorithms that improve the resolution of particle tracking in practice.