  We introduce a novel technique to mitigate the adverse effects of atmospheric
turbulence on astronomical imaging. Utilizing a video-to-image neural network
trained on simulated data, our method processes a sliding sequence of
short-exposure ($\sim$0.2s) stellar field images to reconstruct an image devoid
of both turbulence and noise. We demonstrate the method with simulated and
observed stellar fields, and show that the brief exposure sequence allows the
network to accurately associate speckles to their originating stars and
effectively disentangle light from adjacent sources across a range of seeing
conditions, all while preserving flux to a lower signal-to-noise ratio than an
average stack. This approach results in a marked improvement in angular
resolution without compromising the astrometric stability of the final image.
