Adaptive nonuniformity correction in IRFPA using maximum likelihood estimation

The performance of infrared focal plane array (IRFPA) is known to be affected by the presence of spatial fixed pattern noise (FPN) that is superimposed on the true image. Scene-based nonuniformity correction (NUC) algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its normal operation. A novel adaptive NUC algorithm is presented. The nonuniformity correction is considered as separation of intrinsic images from image sequences. As intrinsic image, the slowly-drifting nonuniformity parameters are presumed to be constant in a short time. The separation is still ill-posed, and here maximum likelihood estimation is suggested to solve it. Following the statistics of infrared images, a prior is used assuming that corrected images will give rise to sparse filter outputs. The performance of the proposed algorithm is evaluated with infrared image sequences with simulated and real fixed pattern noise.