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Application of tilt correlation statistics to anisoplanatic optical turbulence modeling and mitigation

Abstract

Atmospheric optical turbulence can be a significant source of image degradation, particularly in long range imaging applications. Many turbulence mitigation algorithms rely on an optical transfer function (OTF) model that includes the Fried parameter. We present anisoplanatic tilt statistics for spherical wave propagation. We transform these into 2D autocorrelation functions that can inform turbulence modeling and mitigation algorithms. Using these, we construct an OTF model that accounts for image registration. We also propose a spectral ratio Fried parameter estimation algorithm that is robust to camera motion and requires no specialized scene content or sources. We employ the Fried parameter estimation and OTF model for turbulence mitigation. A numerical wave-propagation turbulence simulator is used to generate data to quantitatively validate the proposed methods. Results with real camera data are also presented.

© 2021 Optical Society of America

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Data Availability

The truth images used here may be obtained in Refs. [34,35]. The data with turbulence were simulated using the method in Ref. [22]. The real camera data have not been approved for public release by AFRL at this time.

34. E. Agustsson and R. Timofte, “NTIRE 2017 challenge on single image super-resolution: dataset and study,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017).

35. “University of Southern California Vol. 3: Miscellaneous,” http://sipi.usc.edu/database/.

22. R. C. Hardie, J. D. Power, D. A. LeMaster, D. R. Droege, S. Gladysz, and S. Bose-Pillai, “Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis,” Opt. Eng. 56, 1–16 (2017). [CrossRef]  

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