Calibration of CCD arrays for identifying bad pixels and achieving nonuniformity correction is commonly accomplished using dark frames. This kind of calibration technique does not achieve radiometric calibration of the array since only the relative response of the detectors is computed. For this, a second calibration is sometimes utilized by looking at sources with known radiances. This process can be used to calibrate photodetectors as long as a calibration source is available and is well-characterized. A previous attempt at creating a procedure for calibrating a photodetector using the underlying Poisson nature of the photodetection required calculations of the skewness of the photodetector measurements. Reliance on the third moment of measurement meant that thousands of samples would be required in some cases to compute that moment. A photocalibration procedure is defined that requires only first and second moments of the measurements. The technique is applied to image data containing a known light source so that the accuracy of the technique can be surmised. It is shown that the algorithm can achieve accuracy of nearly 2.7% of the predicted number of photons using only 100 frames of image data.
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.