Spatial and temporal adaptive nonuniformity correction for infrared focal plane arrays

The nonuniformity inherently generated in infrared focal plane arrays (IRFPA) due to the inconsistent offsets of detectors severely degrades the performance of infrared imaging systems. This paper proposes a novel spatial and temporal adaptive nonuniformity correction (NUC) algorithm for the IRFPA, based on a statistical model of the infrared images. After subtracting the local means of an infrared image, the residuals are modeled as a collection of generalized Gaussian random variables with inhomogeneous means. Based on this model, a Maximum Likelihood estimation of the offsets is formally derived, producing an online adaptive temporal filter. The filtering result can be further refined by fusing it with the result of a spatial filter. Therefore, we derive an adaptive Wiener filter to remove the non-uniformity in a single frame and provide an adaptive fusion scheme based on the Minimum Mean Square Error criterion. The overall computational complexity of the proposed NUC algorithm is around O(m n log⁡m n) for an infrared image with the size of m × n, which preserves the potential of the algorithm to be implemented on the board within a thermal camera. Extensive experiments on synthesized and real data have demonstrated the superior performance of the proposed algorithm.