High-speed video imaging via multiplexed temporal gradient snapshot

High-speed imaging is crucial for understanding the transient dynamics of the world, but conventional frame-by-frame video acquisition is limited by specialized hardware and substantial data storage requirements. We introduce "SpeedShot,"a computational imaging framework for efficient high-speed video imaging. SpeedShot features a low-speed dual-camera setup, which simultaneously captures two temporally coded snapshots. Cross-referencing these two snapshots extracts a multiplexed temporal gradient image, producing a compact and multiframe motion representation for video reconstruction. Recognizing the unique temporal-only modulation model, we propose an explicable motion-guided scale-recurrent transformer for video decoding. It exploits cross-scale error maps to bolster the cycle consistency between predicted and observed data. Evaluations on both simulated datasets and real imaging setups demonstrate SpeedShot's effectiveness in video-rate up-conversion, with pronounced improvement over video frame interpolation and deblurring methods. The proposed framework is compatible with commercial low-speed cameras, offering a versatile low-bandwidth alternative for video-related applications, such as video surveillance and sports analysis. © The Authors. Published by SPIE and CLP under a Creative Commons Attribution 4.0 International License.