Dynamic turbulence mitigation for long-range imaging in the presence of large moving objects

Abstract Long-range imaging with visible or infrared observation systems is typically hampered by atmospheric turbulence. Software-based turbulence mitigation methods aim to stabilize and sharpen such recorded image sequences based on the image data only. 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. Here, we investigate a new approach for turbulence mitigation on background as well as large moving objects under moderate turbulence conditions. In our method, we apply and compare different optical flow algorithms to locally estimate both the apparent and true object motion in image sequences and subsequently apply dynamic super-resolution, image sharpening, and newly developed local stabilization methods to the aligned images. We assess the use of these stabilization methods as well as a new method for occlusion compensation for these conditions. The proposed methods are qualitatively evaluated on several visible light recordings of real-world scenes. We demonstrate that our methods achieve a similar image quality on background elements as our prior methods for static scenes, but at the same time obtain a substantial improvement in image quality and reduction in image artifacts on moving objects. In addition, we show that our stabilization and occlusion compensation methods can be robustly used for turbulence mitigation in imagery featuring complex backgrounds and occlusion effects, without compromising the performance in less challenging conditions