Application of Tilt Correlation Statistics to Anisoplanatic Optical Turbulence Modeling and Mitigation

TL;DR

This paper develops a new anisoplanatic tilt statistics model for spherical wave propagation in atmospheric turbulence, enabling improved turbulence mitigation and Fried parameter estimation in long-range imaging.

Contribution

It introduces a novel anisoplanatic tilt statistics model and a robust spectral-ratio Fried parameter estimation algorithm for turbulence mitigation.

Findings

Validated the tilt statistics model with numerical simulations

Demonstrated the Fried parameter estimation method's robustness to camera motion

Achieved improved turbulence mitigation results with real camera data

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.