# Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function

arXiv: https://arxiv.org/abs/2609.06929 · HTML: https://mlsanity.com/paper/2609.06929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 7 Sep 2026.

## Builds on (most cited first, 1 of 1)

- [One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation](https://mlsanity.com/paper/2204.07992/) (2022)

## Abstract (arXiv)

Inter-frame affine misalignment caused by platform jitter and attitude adjustments poses a fundamental challenge for multi-frame analysis of point targets in optical surveillance. Conventional registration methods rely on spatial intensity correlations or distinctive image features, both of which are largely absent in low-signal-to-noise-ratio point target imagery. We introduce the Radon Point Spread Function (RPSF) to characterize point targets in the Radon-transformed domain, and derive a closed-form framework that jointly estimates inter-frame translation and rotation from as few as four scalar RPSF samples per frame pair. The method requires no iterative optimization, feature extraction or interpolation, which is suitable for resource-constrained onboard processing. Simulation results confirm sub-pixel translation accuracy and a mean rotation error of 0.2556deg at 1deg Radon angular resolution. Validation on five real space debris datasets including both ground-based and in-orbit observations yields a mean calibration error below 0.5 pixels, substantially exceeding the precision required for reliable multi-frame processing.