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 paper, 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.

本文介绍了一种统一的大气湍流衰减方法，在静态和动态序列中均可使用，并且通过利用一种新的时空非局部平均方法来构建可靠的参考帧，利用几何一致性和锐度度量来生成幸运帧，并通过对盲变焦点扩散函数进行物理约束的先验模型。通过合成和真实长距离湍流序列基于实验结果验证了所提出方法的性能。