Joint bias and gain nonuniformity correction of infrared videos using tensorial-RLS technique

Infrared (IR) focal-plane array (FPA) detectors suffer from fixed-pattern noise (FPN), also known as spatial nonuniformity, which degrades image quality. In fact, FPN remains a serious problem despite recent advances in IRFPA technology. This work proposes a scene-based correction algorithm to continuously compensate for bias and gain nonuniformity in focal-plane array sensors. The proposed technique is a recursive algorithm based on recursive least square (RLS) techniques that jointly compensates for both bias and gain for each image pixel. The method converges rapidly and presents robustness to noise. Experiments with synthetic and real IRFPA videos has shown that it is competitive with the state-of-the-art in FPN reduction, presenting recovered images with higher fidelity when compared to them.