  Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing
technology with extreme low-light sensitivity and picosecond timing resolution.
These unique capabilities have enabled SPADs to be used in applications like
LiDAR, non-line-of-sight imaging and fluorescence microscopy that require
imaging in photon-starved scenarios. In this work we harness these capabilities
for dealing with motion blur in a passive imaging setting in low illumination
conditions. Our key insight is that the data captured by a SPAD array camera
can be represented as a 3D spatio-temporal tensor of photon detection events
which can be integrated along arbitrary spatio-temporal trajectories with
dynamically varying integration windows, depending on scene motion. We propose
an algorithm that estimates pixel motion from photon timestamp data and
dynamically adapts the integration windows to minimize motion blur. Our
simulation results show the applicability of this algorithm to a variety of
motion profiles including translation, rotation and local object motion. We
also demonstrate the real-world feasibility of our method on data captured
using a 32x32 SPAD camera.
