# Experimental Verification of PCH-EM Algorithm for Characterizing DSERN Image Sensors

## TL;DR

This paper experimentally verifies the PCH-EM algorithm's effectiveness in characterizing DSERN image sensors, demonstrating its accuracy across various exposure and noise levels and validating the PCD model's predictive capability.

## Contribution

It provides a comprehensive experimental validation of the PCH-EM algorithm for DSERN sensors and confirms the PCD model's accuracy in predicting sensor behavior.

## Key findings

- PCH-EM accurately characterizes DSERN pixels across a wide range of conditions.
- The PCD model effectively predicts the ensemble distribution of the sensor.
- Experimental results align well with model predictions, confirming their applicability.

## Abstract

The Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm has recently been reported as a candidate method for the characterization of Deep Sub-Electron Read Noise (DSERN) image sensors. This work describes a comprehensive demonstration of the PCH-EM algorithm applied to a DSERN capable quanta image sensor. The results show that PCH-EM is able to characterize DSERN pixels for a large span of quanta exposure and read noise values. The per-pixel characterization results of the sensor are combined with the proposed Photon Counting Distribution (PCD) model to demonstrate the ability of PCH-EM to predict the ensemble distribution of the device. The agreement between experimental observations and model predictions demonstrates both the applicability of the PCD model in the DSERN regime as well as the ability of the PCH-EM algorithm to accurately estimate the underlying model parameters.