Software-based turbulence mitigation of short exposure image data with motion detection and background segmentation

The degree of image degradation due to atmospheric turbulence is particularly severe when imaging over long horizontal paths since the turbulence is strongest close to the ground. The most pronounced effects include image blurring and image dancing and in case of strong turbulence image distortion as well. To mitigate these effects a number of methods from the field of image processing have been proposed most of which aim exclusively at the restoration of static scenes. But there is also an increasing interest in advancing turbulence mitigation to encompass moving objects as well. Therefore, in this paper a procedure is described that employs block-matching for the segmentation of static scene elements and moving objects such that image restoration can be carried out for both separately. This way motion blurring is taken into account in addition to atmospheric blurring, effectively reducing motion artefacts and improving the overall restoration result. Motion-compensated averaging with subsequent blind deconvolution is used for the actual image restoration.