Efficient Trajectory Space-Time Super-Resolution for Fast Live-cell Imaging

Live-cell imaging is a powerful tool for studying dynamic subcellular processes by capturing the spatiotemporal organization of the biological microenvironment. However, limitations due to phototoxicity and photobleaching prevent microscopes from achieving high frame rates and high-quality images. Although current deep learning methods can enhance both frame rates and image resolution without compromising cell health, they often overlook the continuity of subcellular trajectories, which leads to discontinuous temporal modeling. It also incurs prohibitive computational costs due to exhaustive correlation computation that hinder real-time applications. To address these issues with high efficiency, we propose Trajectory Space-Time Super-Resolution (T-STSR), a method designed to boost frame rates and resolution in fast subcellular imaging while significantly reducing computational overhead. Our approach incorporates Spatial-Temporal Trajectory Modeling (STTM), which learns a state-space model over spatiotemporal slices to reconstruct particle trajectories at low cost. In addition, our novel Trajectory-Aware Loss randomly subsamples trajectory data during training, promoting continuous trajectory representation and mitigating noise with minimal additional computation. We validated T-STSR on both synthesized and real-world datasets with various particle types and noise conditions, demonstrating that our method achieves superior restoration results while saving 75% inference time compared to the previous SOTA model.