Long-range horizontal path imaging through atmospheric turbulence is hampered by spatiotemporally randomly varying shifting and blurring of scene points in recorded imagery. Although existing software-based mitigation strategies can produce sharp and stable imagery of static scenes, it remains highly challenging to mitigate turbulence in scenes with moving objects such that they remain visible as moving objects in the output. In our work, we investigate if and how event (also called neuromorphic) cameras can be used for this challenge. We explore how the high temporal resolution of the event stream can be used to distinguish between the apparent motion due to turbulence and the actual motion of physical objects in the scene. We use this to propose an algorithm to reconstruct output image sequences in which the static background of the scene is mitigated for turbulence, while the moving objects in the scene are preserved. The algorithm is demonstrated on indoor experimental recordings of moving objects imaged through artificially generated turbulence.

中文翻译：

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减轻图像中的湍流，包括从静态事件摄像机移动物体

通过时空随机变化记录的图像中场景点的移动和模糊，阻碍了通过大气湍流进行的远程水平路径成像。尽管现有的基于软件的缓解策略可以生成静态场景的清晰且稳定的图像，但要减轻具有运动对象的场景中的湍流（使它们在输出中仍可作为运动对象保持可见）仍然具有很高的挑战性。在我们的工作中，我们调查了事件相机（也称为神经形态相机）是否以及如何用于此挑战。我们探索如何使用事件流的高时间分辨率来区分由湍流引起的视在运动与场景中物理对象的实际运动。我们使用它来提出一种算法，以重建输出图像序列，其中场景的静态背景因湍流而得到缓解，而场景中的运动对象得以保留。该算法在通过人工产生的湍流成像的运动物体的室内实验记录中得到了证明。