Nonuniformities in the imaging characteristics of modern image sensors are a primary factor in the push to develop a pixel-level generalization of the photon transfer characterization method. In this paper, we seek to develop a body of theoretical results leading toward a comprehensive approach for tackling the biggest obstacle in the way of this goal: a means of pixel-level conversion gain estimation. This is accomplished by developing an estimator for the reciprocal-difference of normal variances and then using this to construct a novel estimator of the conversion gain. The first two moments of this estimator are derived and used to construct exact and approximate confidence intervals for its absolute relative bias and absolute coefficient of variation, respectively. A means of approximating and computing optimal sample sizes are also discussed and used to demonstrate the process of pixel-level conversion gain estimation for a real image sensor.

翻译：现代图像传感器成像特性的不一致性是推动开发光子传输特征描述方法像素级一般化的首要因素。 在本文中,我们寻求开发一套理论结果,以综合方法解决实现这一目标的最大障碍:像素级转换收益估计手段。这是通过开发一个正常差异的对等差异估计符来实现的,然后用它来构建一个转换收益的新估计符。这个估计符的前两个时刻分别用来为其绝对相对偏差和绝对变异系数建立精确和近似信任间隔。还讨论并使用一种接近和计算最佳样本大小的方法来展示真实图像传感器的像素级转换收益估计过程。