An Improved Non-uniformity Correction Algorithm for IRFPA Based on Neural Network

Aiming at traditional neural networks non-uniformity correction (NUC) algorithm's disadvantages such as slow convergence, low correction precision and difficulty to meet the real-time engineering application requirements of infrared imaging system, an improved NUC algorithm for infrared focal plane arrays (IRFPA) based on neural network is proposed. The algorithm is based on linear response of detector, and in order to realize fast and synchronization convergence of correction parameters the each original image data is normalized to a value close to one. Experimental results show the method has the faster convergence speed and better vision effect than the traditional algorithms, and it is better applied in practical projects.