Ground based long-range passive imaging systems often suffer from degraded image quality due to a turbulent atmosphere. While methods exist for removing such turbulent distortions, many are limited to static sequences which cannot be extended to dynamic scenes. In addition, the physics of the turbulence is often not integrated into the image reconstruction algorithms, making the physics foundations of the methods weak. In this article, we present a unified method for atmospheric turbulence mitigation in both static, and dynamic sequences. We are able to achieve better results compared to existing methods by utilizing (i) a novel space-time non-local averaging method to construct a reliable reference frame, (ii) a geometric consistency, and a sharpness metric to generate the lucky frame, (iii) a physics-constrained prior model of the point spread function for blind deconvolution. Experimental results based on synthetic and real long-range turbulence sequences validate the performance of the proposed method.

中文翻译：

---

通过 Anisoplanatic Turbulence 重建静态和动态场景的图像

由于大气湍流，地基远程无源成像系统通常会降低图像质量。虽然存在消除这种湍流失真的方法，但许多方法仅限于无法扩展到动态场景的静态序列。此外，湍流的物理原理往往没有集成到图像重建算法中，使得这些方法的物理基础薄弱。在本文中，我们提出了一种在静态和动态序列中缓解大气湍流的统一方法。通过利用（i）一种新颖的时空非局部平均方法来构建可靠的参考框架，（ii）几何一致性和锐度度量来生成幸运框架，与现有方法相比，我们能够获得更好的结果，(iii) 用于盲解卷积的点扩散函数的物理约束先验模型。基于合成和真实远程湍流序列的实验结果验证了所提出方法的性能。