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.

Modeling and simulation of multispectral imaging through anisoplanatic atmospheric optical turbulence

“…The same is true for the two-dimensional (2D) tilt correlation, patch tilt variance, and residual tilt variance derived in Ref. 14.…”

“…Here, we demonstrate that the desired correlation extends to the multispectral case. In particular, we compare the theoretical and simulation total tilt correlation 5 , 14 in Fig. 14 for the three simulated wavelengths.…”

“…This method employs the tilt statistics reported in Ref. 14 with added blurring from the average short-exposure OTF. This provides a fast approximate simulation with anisoplanatic warping and isoplanatic blurring.…”

“…This parameter has been used to model the level tilt variance correction achieved when image registration and fusion are employed 12 – 14 Note that α=0 corresponds to no registration prior to image fusion and α=1 corresponds to ideal registration.…”

“…For example, anisoplanatic turbulence simulators have been used recently to provide training data for machine learning-based TM 9 . Such simulators have been used in many other works 10 – 14 for TM algorithm development, tuning, and quantitative performance evaluation. Most of the focus of anisoplantic turbulence modeling, simulation, and mitigation has been for a single wavelength.…”