Abstract
Single-photon cameras (SPCs) are emerging as sensors of choice for various
challenging imaging applications. One class of SPCs based on the single-photon
avalanche diode (SPAD) detects individual photons using an avalanche process;
the raw photon data can then be processed to extract scene information under
extremely low light, high dynamic range, and rapid motion. Yet, single-photon
sensitivity in SPADs comes at a cost -- each photon detection consumes more
energy than that of a CMOS camera. This avalanche power significantly limits
sensor resolution and could restrict widespread adoption of SPAD-based SPCs. We
propose a computational-imaging approach called \emph{photon inhibition} to
address this challenge. Photon inhibition strategically allocates detections in
space and time based on downstream inference task goals and resource
constraints. We develop lightweight, on-sensor computational inhibition
policies that use past photon data to disable SPAD pixels in real-time, to
select the most informative future photons. As case studies, we design policies
tailored for image reconstruction and edge detection, and demonstrate, both via
simulations and real SPC captured data, considerable reduction in photon
detections (over 90\% of photons) while maintaining task performance metrics.
Our work raises the question of ``which photons should be detected?'', and
paves the way for future energy-efficient single-photon imaging.