Photon Counting Histogram Expectation Maximization Algorithm for Characterization of Deep Sub-Electron Read Noise Sensors

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 (Hendrickson and Haefner, 2022).

Keywords

• Clustering algorithms
• conversion gain
• DSERN
• expectation maximization
• Gaussian mixture
• PCH