Adaptive Image-Registration-Based Nonuniformity Correction Algorithm With Ghost Artifacts Eliminating for Infrared Focal Plane Arrays

In this paper, we present a new adaptive image registration nonuniformity correction method with the function of eliminating ghost artifacts. This method assumes that the irradiation of objects stays unchanged during the adjacent two frames´ time interval and then corrects the corresponding pixels of two frames by the result of image registration. With regard to real-time continuous image sequence, we first calculate the displacement vectors based on their row and column projections. Then, by bidirectional image registration, we can get the overlapped area matrices of two frames accurately. Meanwhile, a variance threshold is set to judge the scene classification, and then, displacement revising is selectively added to decrease stripe noise. Considering that moving targets exist, the error function for updating is also accordingly adapted to eliminate the ghost artifacts. By using the least-mean-square-error iteration algorithm, the gain and offset coefficients of the overlapped area can be updated in real time with the image sequence adaptively. The advantages of this algorithm lie in its high efficiency of image processing, low computational complexity, and few ghost artifacts. A clean infrared image sequence and two real infrared datasets with nonuniformity are used to compare the ability and correction performance of different algorithms. The results show that our algorithm performs better than other compared algorithms and obtains almost no ghost artifacts.