Photo-detector arrays have imperfections that cause response differences between pixels, incurring a need for Non- Uniformity Correction (NUC) to be normalized. Static Scene Statistical Non-Uniformity Correction (S3NUC) was developed as method that takes advantage of higher order statistical moments to achieve NUC without the use of dedicated calibration hardware, and without sacrificing accuracy. Data in a photo-detector array is modeled as a Poisson random process that is changed by a system gain, bias, and readout noise. While successful, the first iteration of this method relied on higher order moments to reach an overdetermined system of equations that allows the noise to be recovered. Because the third moment is relied upon for a solution, a very large data set with high calculation time is required. By treating the statistics of one Poisson data set as proportional to the integration time, the number of variables can be reduced and allow for a perfectly determined system that relies only on the mean and variance of the data. This assumption is particularly well suited for space object detection, where the scene is stationary enough for two data sets to be collected which vary only by controlled integration time. This new algorithm is tested against the S3NUC algorithm in simulated data to find the errors in noise recovery with respect to the size of the data set. This new SANUC algorithm will be compared in speed in calculation and error in the recovered gain, bias, and readout noise.