Scene-Based Nonuniformity Correction Method Using the Inverse Covariance Form of the Kalman Filter

A scene-based algorithm for nonuniformity correction (NUC) in focalplane arrays (FPA) detectors has been developed. The NUC technique is based in the inverse covariance form (ICF) of the Kalman filter. The gain and the offset of each detector of the FPA are modeled by discrete-time Gauss-Markov processes. These parameters are taken as constant within a given sequence of frames, corresponding to a certain time and operational conditions, but they randomly drift from one sequence to another in response to new operational conditions. For each detector and each sequence of frames, the ICF filter input is an observation vector consisting of detector’s read-out values. The output of ICF filter for any sequence of infrared frames is the detectors’ gain and offset. The efficacy of the ICF of the Kalman filter to compensate for nonuniformity noise in infrared imagery is demonstrated using sequences of infrared imagery with both artificial nonuniformity and artificial drift in the detectors’ parameters. It is shown that the ICF filter and the Kalman filter generate similar reductions of nonuniformity. However, the ICF filter compensates the noisy images with less number of operations per pixel and per frame than the Kalman filter.