Performance study on point target detection using super-resolution reconstruction

When bright moving objects are viewed with an electro-optical system at very long range, they will appear as small slightly blurred moving points in the recorded image sequence. Detection of point targets is seriously hampered by structure in the background, temporal noise and aliasing artifacts due to undersampling by the infrared (IR) sensor. Usually, the first step of point target detection is to suppress the clutter of the stationary background in the image. This clutter suppression step should remove the information of the static background while preserving the target signal energy. Recently we proposed to use super-resolution reconstruction (SR) in the background suppression step. This has three advantages: a better prediction of the aliasing contribution allows a better clutter reduction, the resulting temporal noise is lower and the point target energy is better preserved. In this paper the performance of the point target detection based on super-resolution reconstruction (SR) is evaluated. We compare the use of robust versus non robust SR reconstruction and evaluate the effect of regularization. Both of these effects are influenced by the number of frames used for the SR reconstruction and the apparent motion of the point target. We found that SR improves the detection efficiency, that robust SR outperforms non-robust SR, and that regularization decreases the detection performance. Therefore, for point target detection one can best use a robust SR algorithm with little or no regularization.