In the context of tack领 the ill-posed inverse problem of motion estimation from
image sequences, we propose to introduce prior knowledge on
ow regularity given by
turbulence statistical models. Prior regularity is formalized using turbulence power
laws describing statistically self-similar structure of motion increments across scales.
The motion estimation method minimizes the error of an image observation model
while constraining second order structure function to behave as a power law within a
prescribed range. Thanks to a Bayesian mode领 framework, the motion estimation
method is able to jointly infer the most likely power law directly from image data. The
method is assessed on velocity elds of 2D or quasi-2D
ows. Estimation accuracy
is rst evaluated on a synthetic image sequence of homogeneous and isotropic 2D
turbulence. Results obtained with the approach based on physics of
uids outperforms
state-of-the-art. Then, the method analyzes atmospheric turbulence using a real
meteorological image sequence. Selecting the most likely power law model enables the
recovery of physical quantities which are of major interest for turbulence atmospheric
characterization. In particular, from meteorological images we are able to estimate
energy and enstrophy
uxes of turbulent cascades, which are in agreement with
previous in situ measurements. 德国LaVision PIV/PLIF粒子成像测速场仪 Imager sCMOS PIV相机 用于粒子成像测速(PIV)的荧光示踪粒子
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