Scene-Based Nonuniformity Correction with Adaptively Adjusting Integration Time

Nonuniform response and lower dynamic range are two prevalent issues in the infrared focal plane array (IRFPA) imaging system. Adjusting the integration time is an efficient way to improve the dynamic range of IRFPA imaging system. However, in scene-based nonuniformity correction (SBNUC), with the change of integration time, the correction parameters are no longer applied and it needs to re-converge to get new ones. Therefore, a novel algorithm, combining one-point calibration (based on adaptive adjusting integration time) with neural network (NN) correction, is presented in this paper. The method firstly acquires images based on the blackbody source at different integration time and then adopts a linear regression technique to estimate them as the one-point calibration parameter. Furthermore, it performs the nonuniformity compensation using a neural network approach. In addition, the proposed method only needs to store two regression coefficients. In this way, the algorithm reduces the nonuniformity caused by the change of integration time, as well as denoises the fixed pattern noise (FPN). Experiments on a real infrared video sequence show that the proposed algorithm has improved the convergence rate and the quality of the corrected image, also has the advantage of adaptive adjusting integration time.