Pol-CACTI: A System and dataset forHigh-Speed Polarized Video Compressive Imaging

Capturing high-speed polarization dynamics is crucial forrevealing physical cues like stress variations and surface structures thatare invisible in conventional intensity imaging. While snapshot compres-sive imaging (SCI) enables high-speed video reconstruction from a singlemeasurement, extending it to division-of-focal-plane (DoFP) polarizationsensing introduces a fundamentally more ill-posed inverse problem due tothe coupling between temporal multiplexing and spatial polarization mo-saicing. To address this challenge, we propose Polarization Coded Aper-ture Compressive Temporal Imaging (Pol-CACTI), a hardware–softwareco-designed framework for polarized video SCI. First, we build an opticalsystem that encodes DoFP polarization dynamics into a single snapshotthrough spatiotemporal modulation and integration. Second, we con-struct a large-scale high-quality polarized video dataset with carefullydenoised Stokes-domain supervision, enabling supervised learning of po-larization video SCI reconstruction models. Third, we develop a physics-driven reconstruction network that jointly performs temporal recoveryand polarization demosaicing, where the Polarization-Aware LearnableBack-Projection (PA-LBP) module is proposed to enforce measurementconsistency with the raw DoFP mosaic and mitigates error accumula-tion caused by decoupled reconstruction pipelines. Extensive experimentsdemonstrate that our method significantly improves both intensity fi-delity and polarization estimation accuracy. The dataset and code arereleased to https://github.com/kaeoqxhailesxya/Pol-CACTI.