Non-uniformity correction algorithm based on improved neural network

Aiming at the problem of the traditional neural network for non-uniformity correction easy to cause ghosting artifacts and image blurring, an improved non-uniformity correction algorithm based on neural network is proposed. Firstly, a new fast trilateral filter is designed, which can be regarded as an edge-preserving smoothing operator. Secondly, in order to stabilize and accelerate the learning process, it adopts the self-adaptive learning rate and applies additional momentum factor to the neural network. Thirdly, in order to update the calibration parameters accurately, the local motion of different areas is judged carefully. The simulating experiments indicate that the proposed algorithm can suppress the ghosting artifacts and the image degradation. And it has better performance compared with other algorithms.