MetaVIn: Meteorological and Visual Integration for Atmospheric Turbulence Strength Estimation

Long-range image understanding is a challenging task for computer vision due to the presence of atmospheric turbulence. Turbulence can degrade image quality (blur and geometric distortion) due to the medium's spatio-temporal varying index of refraction bending light rays. The strength of atmospheric turbulence is quantified by the refractive index structure parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_n^2$</tex>, and estimating it is important both as an indicator of image degradation and is useful for downstream tasks including video restoration and estimating true shape and range/depth. However, traditional methods for estimating <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{n}^{2}$</tex> involve expensive and complex optical equipment, limiting their practicality. In this paper, we propose MetaVIn: a Meteorological and Visual Integration system to predict atmospheric turbulence strength. Our method leverages image quality metrics to capture sharpness and blur, combined with meteorological information within a Kolmogorov Arnold Network (KAN). We demonstrate that this approach provides a more accurate and generalizable estimation of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_{n}^{2}$</tex> , outperforming previous state-of-the-art methods in both blind image quality assessment and passive video-based turbulence strength estimation on a large dataset of 35,364 image samples with accompanying ground truth scintillometer measurements for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C_n^2$</tex> . Our method enables better prediction and mitigation of atmospheric image degradation while being useful in applications such as shape and range estimation, enhancing the practical utility of our approach.