Comparison of non-uniformity correction methods in midwave infrared focal plane arrays of high speed platforms

Abstract

Non-uniformity of mid-wave infrared focal plane arrays (FPAs) is a critical factor affecting the detection range of infrared imaging systems, especially in detecting small and dim objects. Moreover, false alarm probability of the system gets higher as the non-uniformity of the FPA increases. Therefore, to improve the performance of infrared imaging systems, non-uniformity of the array should be corrected. Conventionally, to achieve this goal, either scene or calibration based non-uniformity correction (NUC) methodologies have been used. To achieve reasonable performance, scene based NUC techniques may require an impractical amount of time considering the operational duration of high speed platforms. On the other hand, calibration based NUC performance degrades as scene temperature observed by infrared imaging systems of high speed platforms varies. The method presented in this work relies on multiple NUC tables to compensate for the temperature variations in the scene. To compare this method with conventional methods, standard deviation of the NUC images and target detection probability were used as metrics. NUC images were obtained by capturing images with an infrared imaging module. To obtain images with target, either synthetic or pinhole target images were added to the non-uniformity corrected background images. According to these metrics, we found that multiple point NUC correction method is superior to the conventional calibration based method using single NUC table.