Adaptive Bias Compensation for Non-Uniformity Correction on Infrared Focal Plane Array Detectors

The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with a fixed-pattern noise. In this paper we present a new adaptive scene-based non-uniformity correction (NUC) technique. The method simultaneously estimates detector’s parameters and performs the non-uniformity compensation using a neural approach and a Kalman estimator in a frame by frame recursive basis. Each detector’s output is connected to its own inverse model: a single 1-input linear neuron. The neuron bias is directly related to the detector’s offset, and have the property of being softly adapted using simple learning rules, choosing a suitable error measure to fit the NUC objective. The proposed method has been tested with sequences of real infrared data taken with a InSb IRFPA, reaching high correction levels, reducing the fixed pattern noise, and obtaining an effective frame by frame adaptive estimation of each detector’s offset.