Simulation of atmospheric turbulence for a qualitative evaluation of image restoration algorithms with motion detection

Remote sensing applications are generally concerned with observing objects over long distances. When imaging over long horizontal paths, image resolution is limited by the atmosphere rather than by the design and quality of the optical system being used. Atmospheric turbulence can cause quite severe image degradation, the foremost effects being blurring and image motion. Recently, interest in image processing solutions has been rising, not least of all because of the comparatively low cost of computational power, and also due to an increasing number of imaging applications that require the correction of extended objects rather than point-like sources only. 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. However, an unbiased qualitative evaluation of the respective restoration results proves difficult if little or no additional information on the "true image" is available. Therefore, in this paper synthetic ground truth data containing moving vehicles were generated and a first-order atmospheric propagation simulation was implemented in order to test such algorithms. The simulation employs only one phase screen and assumes isoplanatic conditions (only global image motion) while scintillation effects are ignored.