Dynamic turbulence mitigation with large moving objects

Abstract

Long range imaging with visible or infrared observation systems is typically hampered by atmospheric turbulence. The fluctuations in the refractive index of the air produce random shifts and blurs in the recorded imagery that vary across the field of view and over time. This severely complicates their utility for visual detection, recognition and identification at large distances. Software based turbulence mitigation methods aim to restore such recorded image sequences based on the image data only and thereby enable visual identification at larger distances. Although successful restoration has been achieved on static scenes in the past, a significant challenge remains in accounting for moving objects such that they remain visible as moving objects in the output. Under moderate turbulence conditions, the turbulence induced shifts may be several pixels in magnitude and occur on the same length scale as moving objects. This severely complicates the segmentation between these objects and the background. Here we investigate how turbulence mitigation may be accomplished on background as well as large moving objects for both land and sea based imaging under moderate turbulence conditions. We apply optical flow estimation methods to determine both the turbulence induced shifts in image sequences as well as the motion of large moving objects. These motion estimates are used with our TNO turbulence mitigation software to reduce the effects of turbulence and to stabilize the output to a dynamic reference. We apply this approach to both land and sea scenarios. We investigate how different regularization methods for the optical flow affect the accuracy of the segmentation between moving object motion and the background motion. Moreover we qualitatively asses the quality improvement of the resulting imagery in sequences of output images, and show a substantial gain in their apparent sharpness and stability on both the background and moving objects.