Turbulence Strength $C_n^2$ Estimation from Video using Physics-based Deep Learning

TL;DR

This paper introduces a physics-based deep learning approach for estimating turbulence strength $C_n^2$ from video, combining classical image gradient methods with neural networks to improve accuracy and generalization.

Contribution

The paper proposes a novel physics-based neural network architecture that integrates learned features with classical gradient methods for better $C_n^2$ estimation.

Findings

Deep learning methods outperform classical approaches in accuracy.

The physics-based network generalizes better across different datasets.

A new dataset with video and scintillometer measurements is released.

Abstract

Images captured from a long distance suffer from dynamic image distortion due to turbulent flow of air cells with random temperatures, and thus refractive indices. This phenomenon, known as image dancing, is commonly characterized by its refractive-index structure constant $C_n^2$ as a measure of the turbulence strength. For many applications such as atmospheric forecast model, long-range/astronomy imaging, and aviation safety, optical communication technology, $C_n^2$ estimation is critical for accurately sensing the turbulent environment. Previous methods for $C_n^2$ estimation include estimation from meteorological data (temperature, relative humidity, wind shear, etc.) for single-point measurements, two-ended pathlength measurements from optical scintillometer for path-averaged $C_n^2$, and more recently estimating $C_n^2$ from passive video cameras for low cost and hardware complexity. In this paper, we present a comparative analysis of classical image gradient methods for $C_n^2$ estimation and modern deep learning-based methods leveraging convolutional neural networks. To enable this, we collect a dataset of video capture along with reference scintillometer measurements for ground truth, and we release this unique dataset to the scientific community. We observe that deep learning methods can achieve higher accuracy when trained on similar data, but suffer from generalization errors to other, unseen imagery as compared to classical methods. To overcome this trade-off, we present a novel physics-based network architecture that combines learned convolutional layers with a differentiable image gradient method that maintains high accuracy while being generalizable across image datasets.