Adaptive non-uniformity correction based on multi model particle filter

Abstract

In the process of scene based non-uniformity correction, the scene is often changeable, and the corresponding detector operating range of different temperature scenes is different, and their nonuniformity parameters are also different, so when the scene changes, the non-uniformity parameters will also drift, and it is easy to introduce serious ghosts in the correction results, which greatly reduces the Rate of convergence of the algorithm, This poses serious problems for real-time non-uniformity correction algorithms. At the same time, the scene is very flexible, and a single model cannot describe the scene well. Therefore, on the basis of treating the dynamic change process of the scene as a Markov process, this article proposes an adaptive non-uniformity correction algorithm based on Multi Model Particle Filter (PF-NUC). By introducing a tracking framework of particle filter, a nonlinear and non Gaussian parameter estimation model is established. Through experimental simulation verification, after the algorithm proposed in this article, almost no ghosts or residual non-uniformity can be seen. Through visual evaluation, the PF-NUC method has the best ability to remove fixed pattern noise.