Superresolution reconstruction and its impact on sensor performance

Superresolution reconstruction algorithms are increasingly being proposed as enhancements for low resolution electro-optical and thermal sensors. These algorithms exploit either random or programmed motion of the sensor along with some form of estimation to provide a higher density sampling of the scene. In this paper, we investigate the impact of superresolution processing on observer performance. We perform a detailed analysis of the quality of reconstructed images under a variety of scene conditions and algorithm parameters with respect to human performance of a well defined task; target identification of military vehicles. Imagery having synthetic motion is used with the algorithm to produce a series of static images. These images were used in a human perception study of target identification performance. Model predictions were compared with task performance. The implication of these results on the improvement of models to predict sensor performance with superresolution is discussed.