Abstract
Fluctuations of the refractive index in the atmosphere, known as optical turbulence, impact a wide variety of optical and imaging systems. These refractive index fluctuations are driven primarily by fluctuations in temperature, and are typically quantified by the refractive index structure constant Cn2. This work uses machine learning to examine several gradient and perception-based image features with regard to their ability to estimate Cn2, both independently and in concert with other accessible quantities.
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