Uncooled infrared focal plane array detector module with the self-adaptive non-uniformity correction function

Non-uniformity correction (NUC) for the IRFPA has become one of the most important issues for defense electro-optics. Scene-based non-uniformity correction (SBNUC) is the major trend of NUC techniques in the future. In this paper, the author introduces the research progress in SBNUC technique based on the constant statistic constraint, neural network, and motion estimation. The method to combine SBNUC algorithms with infrared focal plate array (IRFPA) detector module is presented, and the uncooled IRFPA detector module with self-adaptive non-uniformity correction function is developed. The proposed algorithms can use only image sequence information to calculate the gain/bias parameters and update them group-by-group or frame-by-frame, compensating the FPN temporal variation effectively. The detector module can improve the image performance of the imaging system significantly, and ensure the stability of the imaging system with the scene motion dynamically.