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。

靠各向异性湍流重构静态和动态场景的图像

Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic  Turbulence

We address the problem of restoring a high-quality image from an observed
image sequence strongly distorted by atmospheric turbulence. A novel algorithm
is proposed in this paper to reduce geometric distortion as well as
space-and-time-varying blur due to strong turbulence. By considering a suitable
energy functional, our algorithm first obtains a sharp reference image and a
subsampled image sequence containing sharp and mildly distorted image frames
with respect to the reference image. The subsampled image sequence is then
stabilized by applying the Robust Principal Component Analysis (RPCA) on the
deformation fields between image frames and warping the image frames by a
quasiconformal map associated with the low-rank part of the deformation matrix.
After image frames are registered to the reference image, the low-rank part of
them are deblurred via a blind deconvolution, and the deblurred frames are then
fused with the enhanced sparse part. Experiments have been carried out on both
synthetic and real turbulence-distorted video. Results demonstrate that our
method is effective in alleviating distortions and blur, restoring image
details and enhancing visual quality.

本文提出了一种新颖的算法来降低由大气湍流引起的几何失真以及时空变化的模糊，该算法首先利用适当的能量函数得到一个清晰度较高的参考图像以及一个包含参考图像的清晰和略微失真图像帧的子采样图像序列，然后通过稳定变形场来稳定子采样图像序列，最后应用鲁棒主成分分析和盲反卷积技术去模糊，融合图像帧，实验表明该方法在恢复图像细节和提高视觉质量方面有效。