Turbulence exists widely in the natural atmosphere and in industrial fluids. Strong randomness, anisotropy and mixing of multiple-scale eddies complicate the analysis and measurement of atmospheric turbulence. Although the spatially integrated strength of atmospheric turbulence can be roughly measured indirectly by Doppler radar or laser, direct measurement of two-dimensional (2D) strength fields of atmospheric turbulence is challenging. Here we attempt to solve this problem through infrared imaging. Specifically, we propose a physically boosted cooperative learning framework, termed the PBCL, to quantify 2D turbulence strength from infrared images. To demonstrate the capability of the PBCL, we constructed a dataset with 137,336 infrared images and corresponding 2D turbulence strength fields. The experimental results show that cooperative learning brings performance improvements, enabling the PBCL to simultaneously learn turbulence strength fields and inhibit adverse turbulence effects in images. Our work demonstrates the potential of imaging in measuring physical quantity fields.
© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.