Working from a Poisson-Gaussian noise model, a multi-sample extension of the Photon Counting Histogram Expectation Maximization (PCH-EM) algorithm is derived as a general-purpose alternative to the Photon Transfer (PT) method. This algorithm is derived from the same model, requires the same experimental data, and estimates the same sensor performance parameters as the time-tested PT method, all while obtaining lower uncertainty estimates. It is shown that as read noise becomes large, multiple data samples are necessary to capture enough information about the parameters of a device under test, justifying the need for a multi-sample extension. An estimation procedure is devised consisting of initial PT characterization followed by repeated iteration of PCH-EM to demonstrate the improvement in estimate uncertainty achievable with PCH-EM; particularly in the regime of Deep Sub-Electron Read Noise (DSERN). A statistical argument based on the information theoretic concept of sufficiency is formulated to explain how PT data reduction procedures discard information contained in raw sensor data, thus explaining why the proposed algorithm is able to obtain lower uncertainty estimates of key sensor performance parameters such as read noise and conversion gain. Experimental data captured from a CMOS quanta image sensor with DSERN is then used to demonstrate the algorithm's usage and validate the underlying theory and statistical model. In support of the reproducible research effort, the code associated with this work can be obtained on the MathWorks File Exchange (Hendrickson et al., 2024).

翻译：基于泊松-高斯噪声模型，推导出光子计数直方图期望最大化（PCH-EM）算法的多样本扩展，作为光子转移（PT）方法的通用替代方案。该算法与经过时间检验的PT方法基于相同模型，需要相同实验数据，并估计相同的传感器性能参数，同时能获得更低的不确定性估计。研究表明，当读出噪声较大时，需要多个数据样本来捕获被测设备参数的足够信息，这证明了多样本扩展的必要性。本文设计了一种估计流程，包括初始PT表征及后续PCH-EM的重复迭代，以展示PCH-EM在估计不确定性方面的改进；特别是在深亚电子读出噪声（DSERN）区域。基于信息论中充分性概念的统计论证被用来解释PT数据降维过程如何丢弃原始传感器数据中包含的信息，从而解释了为何所提算法能够获得关键传感器性能参数（如读出噪声和转换增益）的更低不确定性估计。随后，使用从具有DSERN的CMOS量子图像传感器捕获的实验数据来演示该算法的使用，并验证底层理论和统计模型。为支持可重复研究工作，本工作相关代码可从MathWorks文件交换中心获取（Hendrickson等，2024）。