Diffusion in SPAD Signals
This work addresses signal processing challenges in SPAD-based imaging, but it appears incremental as it applies existing diffusion methods to a specific sensor type.
The authors tackled the problem of modeling raw signals from single photon avalanche diodes (SPADs) by deriving the likelihood and score function for detection event timings, which are nonlinearly and stochastically related to photon flux, enabling inverse problem solutions with diffusion models for image priors.
We derive the likelihood of a raw signal in a single photon avalanche diode (SPAD), given a fixed photon flux. The raw signal comprises timing of detection events, which are nonlinearly related to the flux. Moreover, they are naturally stochastic. We then derive a score function of the signal. This is a key for solving inverse problems based on SPAD signals. We focus on deriving solutions involving a diffusion model, to express image priors. We demonstrate the effect of low or high photon counts, and the consequence of exploiting timing of detection events.