Motion Adaptive Deblurring with Single-Photon Cameras

Trevor Seets, Atul Ingle, Martin Laurenzis, Andreas Velten

Abstract:

Single-photon avalanche diodes (SPADs) are a rapidly
developing image sensing technology with extreme lowlight 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 32 × 32 SPAD camera.