Problem
Research questions and friction points this paper is trying to address.
affine misalignment
platform jitter
low-signal-to-noise-ratio
Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function
🤖 AI Summary
本文提出了一种基于Radon点扩散函数的方法，用于解决空间碎片图像因平台抖动和姿态调整导致的帧间仿射错位问题，无需迭代优化或特征提取。
📝 Abstract
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 0.2556{\deg} at 1{\deg} 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Radon Point Spread Function
sub-pixel affine registration
closed-form framework
resource-constrained onboard processing
🔎 Similar Papers
No similar papers found.
