  The ability to record high-fidelity videos at high acquisition rates is
central to the study of fast moving phenomena. The difficulty of imaging fast
moving scenes lies in a trade-off between motion blur and underexposure noise:
On the one hand, recordings with long exposure times suffer from motion blur
effects caused by movements in the recorded scene. On the other hand, the
amount of light reaching camera photosensors decreases with exposure times so
that short-exposure recordings suffer from underexposure noise. In this paper,
we propose to address this trade-off by treating the problem of high-speed
imaging as an underexposed image denoising problem. We combine recent advances
on underexposed image denoising using deep learning and adapt these methods to
the specificity of the high-speed imaging problem. Leveraging large external
datasets with a sensor-specific noise model, our method is able to speedup the
acquisition rate of a High-Speed Camera over one order of magnitude while
maintaining similar image quality.
