MetaVIn: Meteorological and Visual Integration for Atmospheric Turbulence Strength Estimation

Ripon Kumar Saha, Scott McCloskey, Suren Jayasuriya; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 8565-8574

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 Cn2 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 Cn2 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 Cn2 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 35364 image samples with accompanying ground truth scintillometer measurements for Cn2. 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.