Imaging through Atmospheric Turbulence

Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence

IEEE Transactions on Computational Imaging, vol. 6, pp. 1415-1428, Oct. 2020.
Manuscript: https://arxiv.org/abs/2009.00071
Zhiyuan Mao, Nicholas Chimitt, and Stanley H. Chan

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 algorithm. 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.

MATLAB Implementation: (Coming Soon)

Simulating Anisoplanatic Turbulence by Sampling Correlated Zernike Coefficients

SPIE Optical Engineering 2020 (a shorter version is presented in ICCP 2020)
Journal Version: https://arxiv.org/abs/2004.11210
Conference Version: https://ieeexplore.ieee.org/document/9105270
Nicholas Chimitt and Stanley H. Chan

Simulating atmospheric turbulence is an essential task for evaluating turbulence mitigation algorithms and training learning-based methods. Advanced numerical simulators for atmospheric turbulence are available, but they require evaluating wave propagation which is computationally expensive. In this paper, we present a propagation-free method for simulating imaging through turbulence. The key idea behind our work is a new method to draw inter-modal and spatially correlated Zernike coefficients. By establishing the equivalence between the angle-of-arrival correlation by Basu, McCrae and Fiorino (2015) and the multi-aperture correlation by Chanan (1992), we show that the Zernike coefficients can be drawn according to a covariance matrix defining the correlations. We propose fast and scalable sampling strategies to draw these samples. The new method allows us to compress the wave propagation problem into a sampling problem, hence making the new simulator significantly faster than existing ones. Experimental results show that the simulator has an excellent match with the theory and real turbulence data.

MATLAB Implementation: (URL)