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

Influenced by detector materials' non-uniformity, growth and etching techniques' etc, every detector's responsivity of infrared focal plane arrays (IRFPA) is different, which results in non-uniformity of IRFPA. And non-uniformity of IRFPA generates fixed pattern noises (FPN) that are superposed on infrared images. So the quality of infrared images is poor, which greatly limited the application of IRFPA. And non-uniformity correction (NUC) is an important technique for IRFPA. The traditional non-uniformity correction algorithm based on neural network and its modified algorithms are analyzed in this paper. And a new improved non-uniformity correction algorithm based on neural network is proposed in this paper. In this algorithm, the desired image is estimated by using three successive images in an infrared sequence. And motion blur is avoided by applying implicit motion detection and edge detection. So the estimation image is closer to real image than the estimation image estimated by other algorithms. So convergence speed of correction parameters is much fast. A comparison is made to these algorithms in this paper. And experimental results show that the algorithm proposed in this paper prevails over other algorithms based on neural network.