Scene-based Non-uniformity Correction using Complementary Fixed Pattern Noise Models

We propose a novel scene-based non-uniformity correction (NUC) scheme for infrared focal plane array (FPA) detectors to account for both high-frequency and low-frequency fixed pattern noise (FPN). High-frequency FPN can be significantly reduced by the recent scene based NUC algorithms. However, low-frequency FPN caused by stray light, optical effects, heat dissipation and so forth, is commonly compensated by calibration based NUC methods. In this work, we aim to reduce both the low-frequency and high frequency components of FPN by using an efficient combination of registration based and constant statistics based approaches. We exploit scene variations through the video sequence and find the underlying low-frequency noise by smartly averaging frames based on their motion and detail content. Thus, we add the generalization power of constant statistics approach to existing scene-based NUC methods to obtain lower FPN in both high-frequency and low-frequency components. The performance of the proposed method is experimented on a public dataset corrupted by real FPN and evaluated by PSNR metric in comparison to a state-of-the-art scene-based NUC method.