Polarimetric imaging captures surface polarization characteristics, such as the Degree of Linear Polarization (DoLP) and the Angle of Polarization (AoP). In mainstream Division of-Focal-Plane (DoFP) color polarization imaging, recovering polarization parameters from captured mosaic arrays remains a challenging inverse problem. Existing DoFP cameras also face hardware bottlenecks and often cannot support high-frame-rate acquisition, limiting polarimetric imaging in dynamic video tasks. These limitations motivate joint spatial and temporal enhancement. To this end, we propose the first space-time polarization video reconstruction architecture. The method jointly models polarization directions in space and time and uses a polarization-aware implicit neural representation for continuous, high-fidelity upsampling. By analyzing temporal variations in polarization parameters, we further introduce a flow-guided polarization variation loss to supervise polarization dynamics. We also establish the first large-scale color DoFP polarization video benchmark to support this research direction. Extensive experiments on this benchmark demonstrate the effectiveness of the method.

翻译：偏振成像能够捕获表面偏振特性，例如线偏振度（DoLP）和偏振角（AoP）。在主流的分焦平面（DoFP）彩色偏振成像中，从捕获的马赛克阵列中恢复偏振参数仍是一项具有挑战性的逆问题。现有DoFP相机存在硬件瓶颈，且通常无法支持高帧率采集，从而限制了偏振成像在动态视频任务中的应用。这些局限性促使我们开展联合时空增强研究。为此，我们首次提出一种时空偏振视频重建架构。该方法联合建模偏振方向的空间与时间维度，并利用偏振感知隐式神经表示实现连续高保真上采样。通过分析偏振参数的时间变化，我们进一步引入流引导的偏振变化损失函数以监督偏振动态。同时，我们建立了首个大规模彩色DoFP偏振视频基准，以支持该研究方向。在该基准上的大量实验验证了该方法的有效性。