Turbulence Strength $C_n^2$ Estimation from Video using Physics-based Deep Learning
Optics Express 30, 40854-40870 (2022) 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 structu...
Saved in:
Main Authors | , , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
29.08.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Optics Express 30, 40854-40870 (2022) 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. |
---|---|
DOI: | 10.48550/arxiv.2408.16623 |