Efficient scene-based method for real-time non-uniformity correction of infrared video sequences

A method combing simple linear and nonlinear filters is proposed for real-time `non-uniformity correction´ of infrared video sequences, which suppresses `ghosting´ artefacts due to both lack of motion and strong edges. In this `least mean square´ (LMS)-based method, a mean filter is used first, when the `fixed pattern noise´ (FPN) level is high, taking advantage of its noise smoothing capability. When the FPN level drops to a low level, a sigma filter is used instead to reduce edge smearing. The sigma filter is also used to detect abnormal pixels like dead pixels and pixels contaminated by impulse noise, in addition to adaptive adjustment of the learning rate, with no extra cost. Experiments with simulated data and real infrared sequences show that the proposed method outperforms several other LMS methods. It is of the same computational complexity as Scribner´s method, which makes it a good candidate for real-time hardware implementation.