A design of real-time scene-based nonuniformity correction system

摘要

Scene-based nonuniformity correction algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its normal operation. A system based on the neural network algorithm is designed for real-time correction, using the framework of foreground and background. FPGA as the foreground performs the regular nonuniformity correction and blind pixel detection. As the background, DSP monitors changes of the scene and updates the correction parameters according to the analysis of the scene. In order to eliminate ghosting artifacts, an edge-directed learning scheme is used. Via testing, the system is capable of tackling 25 frames per second. The performance of the system is evaluated with real infrared imaging sequences. The results show a more reliable fixed-pattern noise reduction, tracking the parameter drift, and presenting a good adaptability to scene changes.