In-Scene Calibration of Poisson Noise Parameters for Phase Image Recovery

In sensor metrology, noise parameters governing the stochastic nature of photon detectors play critical role in characterizing the aleatoric uncertainty of computational imaging systems such as indirect time-of-flight cameras, structured light imaging, and division-of-time polarimetric imaging. Standard calibration procedures exists for extracting the noise parameters using calibration targets, but they are inconvenient or impractical for frequent updates. To keep up with noise parameters that are dynamically affected by sensor settings (e.g. exposure and gain) as well as environmental factors (e.g. temperature), we propose an In-Scene Calibration of Poisson Noise Parameters (ISC-PNP) method that does not require calibration targets. The main challenge lies in the heteroskedastic nature of the noise and the confounding influence of scene content. To address this, our method leverages global joint statistics of Poisson sensor data, which can be interpreted as a binomial random variable. We experimentally confirm that the noise parameters extracted by the proposed ISC-PNP and the standard calibration procedure are well-matched.