Density Estimation from Schlieren Images through Machine Learning. (arXiv:2201.05233v1 [physics.flu-dyn])

This study proposes a radically alternate approach for extracting
quantitative information from schlieren images. The method uses a scaled,
derivative enhanced Gaussian process model to obtain true density estimates
from two corresponding schlieren images with the knife-edge at horizontal and
vertical orientations. We illustrate our approach on schlieren images taken
from a wind tunnel sting model, and a supersonic aircraft in flight.

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