Abstract: An improved nonuniformity correction (NUC) algorithm combining image matching and neural network(NN) for infrared focal plane array sensors was presented. Firstly, nonuniformity of the FPA response was removed by NUC compensation. Then, motion parameters of the image were estimated by matching pairs of image frames. Finally, coefficients were adaptively updated according to bidirectional-renew strategy based on neural network. Image matching technique could effectively avoid faintness when coefficients were updating. Additionally, the bidirectional-renew strategy was used to guarantee coefficients of each pixel be calculated at least once when new image frame came. The new algorithm used image matching technique to get scene motion information, and used neural network for coefficients bidirectional-renew strategy. It had a lower statistical overhead on scenes and approached convergence more quickly than the often used neural network based NUC algorithms. A theoretical analysis was performed on a collection of infrared image frames to study the accuracy of the new NUC algorithm. It proves that it has higher-quality correction ability than simple neural network based NUC algorithm.