Model-based real-time nonuniformity correction in focal plane array detectors

A statistical model for the focal-plane array (FPA) output is developed characterizing the random nature of nonuniformity in time and space. The rationale of this method is that current and past outputs of the FPA bear information about the nonuniformity. Using a statistical algorithm, this hidden information about the random nonuniformity can be extracted and used to restore the true image. The proposed algorithm consists of two main parts. The first part involves a periodic statistical estimation of the model parameters using current data. The second part involves utilizing the estimated parameters in restoring the true image by means of a least mean square FIR filter whose coefficients remain unchanged between the rounds of parameter estimation. This model-based approach exploits the slow drift of the sensors' offset voltage, gain, and circuit noise in order to reduce the necessary computations to a minimum.