用新方法精准对齐太空碎片图像，提升低信噪比下多帧分析精度。
Sub-Pixel Affine Registration of Space Debris Images via the Radon Point Spread Function
- 基于Radon变换的点扩散函数，从少量采样中联合估计帧间平移与旋转。
- 仿真显示亚像素级定位精度，平均旋转误差仅0.2556度。
- 无需迭代优化或特征提取，适合星上实时处理，适用于碎片监测场景。
平台抖动和姿态调整导致光学监视中点目标的多帧图像存在帧间仿射错位，构成核心挑战。传统配准方法依赖空间强度相关性或显著图像特征，在低信噪比点目标图像中几乎失效。本文提出Radon点扩散函数（RPSF），用于表征Radon变换域中的点目标，并推导出闭式框架，仅需每帧对四组标量RPSF样本即可联合估计帧间平移与旋转。该方法无需迭代优化、特征提取或插值，适用于资源受限的星载处理。仿真结果表明，该方法在1度Radon角分辨率下实现亚像素级平移精度，平均旋转误差为0.2556度。在五个真实太空碎片数据集（含地面与在轨观测）上的验证显示，平均校准误差低于0.5像素，显著满足可靠多帧处理所需的精度要求。
原文摘要 · Abstract (English)
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.
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