The dissipation and bending of light waves by atmospheric turbulence adversely affects infrared imaging, leading to grayscale drift, distortion, and blurring. A deep learning method has been developed to both extract the two-dimensional atmospheric turbulence strength fields and obtain clear and stable images from turbulence-distorted infrared images.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Dutton, J. A. & Panofsky, H. A. Clear air turbulence: A mystery may be unfolding: High altitude turbulence poses serious problems for aviation and atmospheric science. Science 167, 937–944 (1970). This article introduces the importance of atmospheric turbulence for aviation and atmospheric science and points out that careful measurements and data processing are needed to understand it.
Browning, K. & Watkins, C. Observations of clear air turbulence by high power radar. Nature 227, 260–263 (1970). This article presents the vertical soundings of atmospheric turbulence using a high-power radar.
Sathe, A. & Mann, J. A review of turbulence measurements using ground-based wind lidars. Atmos. Meas. Tech. 6, 3147–3167 (2013). A review article that summarizes turbulence measurements obtained using ground-based wind lidars.
Zhu, X. & Milanfar, P. Removing atmospheric turbulence via space-invariant deconvolution. IEEE T. Pattern. Anal. 35, 157–170 (2012). This article presents a method that restores a clear image from an image sequence distorted by atmospheric turbulence using multi-frame registration and fusion.
Jin, D. et al. Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning. Nat. Mach. Intell. 3, 876–884 (2021). This article presents a deep learning-based method that neutralizes the effects of atmospheric turbulence on imaging data.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This is a summary of: Wang, Y. et al. Revelation of hidden 2D atmospheric turbulence strength fields from turbulence effects in infrared imaging. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00498-z (2023).
About this article
Cite this article
An imaging-based approach to measure atmospheric turbulence. Nat Comput Sci 3, 673–674 (2023). https://doi.org/10.1038/s43588-023-00501-7