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Density reconstruction from schlieren images through Bayesian nonparametric models (2201.05233v3)
Published 13 Jan 2022 in physics.flu-dyn and cs.CV
Abstract: 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, a supersonic aircraft in flight, and a high-order numerical shock tube simulation.