Event-guided temporally super-resolved synchrotron X-ray imaging

Event cameras, as novel bio-inspired neuromorphic sensors, detect per-pixel brightness changes asynchronously. Despite their growing popularity in various applications, their potential in X-ray imaging remains largely unexplored. Synchrotron-based X-ray imaging plays a significant role in various fields of science, technology and medicine. However, time-resolved imaging still faces several challenges in achieving higher sampling rates and managing the substantial data volume. Here, we introduce an inline dual-camera setup, which leverages a high-speed CMOS camera and an event camera, aiming to temporally super-resolve the sampled frame data using sparse events. To process the data, frames and events are first aligned pixel-by-pixel using feature matching, and then used to train a deep-learning neural network. This network effectively integrates the two modalities to reconstruct the intermediate frames, achieving up to a 6-fold temporal upsampling. Our work demonstrates an event-guided temporal super-resolution approach in the X-ray imaging domain, which unlocks possibilities for future time-resolved experiments.