Thermal Sequence Reconstruction under Coupled Motion Blur and Rolling-Shutter Readout

Dynamic uncooled infrared imaging is affected by the coupled effects of thermal persistence and rolling-shutter (RS) readout, making fast scene dynamics difficult to recover. The non-instantaneous response of microbolometer pixels introduces an exponentially decaying temporal history into each observation, while row-dependent readout couples the resulting temporal blur with geometric distortion. Existing sequence reconstruction methods are designed for visible-light imaging, whereas infrared restoration methods generally target one-to-one recovery and do not jointly model these effects. In this work, we formulate thermal sequence reconstruction through a unified spatiotemporal observation model that captures the coupling between microbolometer thermal persistence and RS readout. Based on this formulation, we develop a physics-guided framework that reconstructs sharp scene states at target times from consecutive degraded observations and maps them to the target global-shutter (GS) geometry. During training, the reconstructed RS states are passed through the forward thermal response model to enforce physical reblur consistency. We further construct a physics-based synthetic benchmark and collect a real-world dataset using two uncooled infrared cameras. Experiments demonstrate improved performance over existing motion-restoration methods and favorable cross-sensor generalization on real uncooled infrared observations.