A novel approach to photon transfer conversion gain estimation

TL;DR

This paper introduces a new theoretical framework and estimator for pixel-level conversion gain in image sensors, addressing nonuniformities and enabling more precise sensor characterization.

Contribution

It develops a novel estimator for pixel-level conversion gain based on the reciprocal-difference of variances, with derived moments and confidence intervals.

Findings

Derived exact and approximate confidence intervals for the estimator.

Presented a method for optimal sample size calculation.

Demonstrated the approach on a real image sensor.

Abstract

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.