Turbulence mitigation of short exposure image data using motion detection and background segmentation

Many remote sensing applications are concerned with observing objects over long horizontal paths and often the atmosphere between observer and object is quite turbulent, especially in arid or semi-arid regions. Depending on the degree of turbulence, atmospheric turbulence can cause quite severe image degradation, the foremost effects being temporal and spatial blurring. And since the observed objects are not necessarily stationary, motion blurring can also factor in the degradation process. At present, the majority of these image processing methods aim exclusively at the restoration of static scenes. But there is a growing interest in enhancing turbulence mitigation methods to include moving objects as well. Therefore, the approach in this paper is to employ block-matching as motion detection algorithm to detect and estimate object motion in order to separate directed movement from turbulence-induced undirected motion. This enables a segmentation of static scene elements and moving objects, provided that the object movement exceeds the turbulence motion. Local image stacking is carried out for the moving elements, thus effectively reducing motion blur created by averaging and improving the overall final image restoration by means of blind deconvolution.