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Neural space-time model (NSTM) is a computational image reconstruction framework that can jointly estimate the scene and its motion dynamics by modeling its spatiotemporal relationship, without data priors or pre-training. It is especially useful for multi-shot imaging systems which sequentially capture multiple measurements and are susceptible to motion artifacts if the scene is dynamic. Neural space-time model exploits the temporal redundancy of dynamic scenes. This concept, widely used in video compression, assumes that a dynamic scene evolves smoothly over adjacent timepoints. By replacing the reconstruction matrix, neural space-time model can remove motion-induced artifacts and resolve sample dynamics, from the same set of raw measurements used for the conventional reconstruction.