  Imaging in low light is difficult because the number of photons arriving at
the sensor is low. Imaging dynamic scenes in low-light environments is even
more difficult because as the scene moves, pixels in adjacent frames need to be
aligned before they can be denoised. Conventional CMOS image sensors (CIS) are
at a particular disadvantage in dynamic low-light settings because the exposure
cannot be too short lest the read noise overwhelms the signal. We propose a
solution using Quanta Image Sensors (QIS) and present a new image
reconstruction algorithm. QIS are single-photon image sensors with photon
counting capabilities. Studies over the past decade have confirmed the
effectiveness of QIS for low-light imaging but reconstruction algorithms for
dynamic scenes in low light remain an open problem. We fill the gap by
proposing a student-teacher training protocol that transfers knowledge from a
motion teacher and a denoising teacher to a student network. We show that
dynamic scenes can be reconstructed from a burst of frames at a photon level of
1 photon per pixel per frame. Experimental results confirm the advantages of
the proposed method compared to existing methods.
