Monday, May 29, 2023

PCH-EM Algorithm for DSERN characterization

Hendrickson et al. have posted two new pre-prints on deep sub-electron read noise (DSERN) characterization. This new algorithm called PCH-EM is used to extract key performance parameters of sensors with sub-electron read noise through a custom implementation of the Expectation Maximization (EM) algorithm. It shows a dramatic improvement over the traditional Photon Transfer (PT) method in the sub-electron noise regime. The authors have some extensions and improvements of the method coming soon as well.

The first pre-print titled "Photon Counting Histogram Expectation Maximization Algorithm for Characterization of Deep Sub-Electron Read Noise Sensors" presents the theory behind their approach.

Abstract: We develop a novel algorithm for characterizing Deep Sub-Electron Read Noise (DSERN) image sensors. This algorithm is able to simultaneously compute maximum likelihood estimates of quanta exposure, conversion gain, bias, and read noise of DSERN pixels from a single sample of data with less uncertainty than the traditional photon transfer method. Methods for estimating the starting point of the algorithm are also provided to allow for automated analysis. Demonstration through Monte Carlo numerical experiments are carried out to show the effectiveness of the proposed technique. In support of the reproducible research effort, all of the simulation and analysis tools developed are available on the MathWorks file exchange.

Authors have released their code here:



The second pre-print titled "Experimental Verification of PCH-EM Algorithm for Characterizing DSERN Image Sensors" presents an application of the PCH-EM algorithm to quanta image sensors.

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.

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