Real-Time Adaptive Background-Oriented Schlieren Imaging Algorithm with Spatiotemporal Variability Using Computer Vision and Particle Image Velocimetry
Keywords:
Algorithms, Spatiotemporal Variability, Computer Vision, Particle Image VelocimetryAbstract
This study presents an advanced Background Oriented Schlieren (BOS) computational system designed for high-efficiency, high-resolution fluid flow visualization on mobile platforms. The system integrates wavelet transformations for multi-scale edge detection, Kalman filtering for noise reduction, and adaptive contrast enhancement using CLAHE. The results demonstrate improvements of 30.6% in edge detection sensitivity, 12.5% in resolution enhancement, and 11% in noise reduction, compared to the standard OpenCV and OpenPIV control algorithm. These metrics were calculated through rigorous image processing techniques, including the Laplacian of the image and gradient magnitude for edge detection, full-width at half-maximum (FWHM) for resolution, signal-to-noise resolution (SNR) for noise, and the Jaccard index for flow visualization accuracy.
The system’s ability to deliver accurate and detailed flow visualizations in real time makes it particularly suited for mobile applications, where computational resources are limited. The combination of wavelet analysis, predictive filtering, and adaptive contrast enhancement offers a robust solution for capturing subtle flow patterns, making this approach a significant advancement in BOS technology, with potential application in experimental fluid dynamics, aerodynamic testing, and environmental monitoring.
References
. Raffel M. Background-oriented schlieren (BOS) techniques. Experiments in Fluids. 2015;56(3). doi:https://doi.org/10.1007/s00348-015-1927-5
. Settles GS. Schlieren and Shadowgraph Techniques. Springer Berlin Heidelberg; 2001. doi:https://doi.org/10.1007/978-3-642-56640-0
. Raffel M, Willert C, Wereley S, Jürgen Kompenhans. Particle Image Velocimetry. Springer Nature; 2007. doi:https://doi.org/10.1007/978-3-540-72308-0
. Richard H, Raffel M. Principle and applications of the background oriented schlieren (BOS) method. Measurement Science and Technology. 2001;12(9):1576-1585. doi:https://doi.org/10.1088/0957-0233/12/9/325
. Wu H, Xu F, Duan Y, et al. Spatio-Temporal Fluid Dynamics Modeling via Physical-Awareness and Parameter Diffusion Guidance. arXiv.org. Published 2024. Accessed August 19, 2024. https://arxiv.org/abs/2403.13850
. Doss N, Wu Y, Yang P, Zhou HH. Optimal estimation of high-dimensional location Gaussian mixtures. arXiv.org. Published 2020. Accessed August 19, 2024. https://arxiv.org/abs/2002.05818
. Pedregosa F, Pedregosa@inria F, Fr, et al. Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot Edouard Duchesnay. Journal of Machine Learning Research. 2011;12(12):2825-2830. https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf
. Xu K, Wen L, Li G, Bo L, Huang Q. Spatiotemporal CNN for Video Object Segmentation. arXiv:190402363 [cs]. Published online April 4, 2019. Accessed December 6, 2022. https://arxiv.org/abs/1904.02363
. Oisin Mac Aodha, Humayun A, Pollefeys M, Brostow GJ. Learning a Confidence Measure for Optical Flow. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2013;35(5):1107-1120. doi:https://doi.org/10.1109/tpami.2012.171
. Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989;11(7):674-693. doi:https://doi.org/10.1109/34.192463
. Introduction to the Discrete Wavelet Transform (DWT).; 2004. https://mil.ufl.edu/nechyba/www/eel6562/course_materials/t5.wavelets/intro_dwt.pdf
. Liu L, Su X, Wang G. On Inversion of Continuous Wavelet Transform. Open Journal of Statistics. 2015;05(07):714-720. doi:https://doi.org/10.4236/ojs.2015.57071
. Unser M. Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing. 1995;4(11):1549-1560. doi:https://doi.org/10.1109/83.469936
. Szeliski R. Computer Vision. Springer London; 2011. doi:https://doi.org/10.1007/978-1-84882-935-0
. Otsu N. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979;9(1):62-66. doi:https://doi.org/10.1109/tsmc.1979.4310076
. Scarano F, Riethmuller ML. Iterative multigrid approach in PIV image processing with discrete window offset. Experiments in Fluids. 1999;26(6):513-523. doi:https://doi.org/10.1007/s003480050318
. Smith SM, Brady JM. SUSAN—A New Approach to Low Level Image Processing. International Journal of Computer Vision. 1997;23(1):45-78. doi:https://doi.org/10.1023/a:1007963824710
. Schwab M, Mayr A, Haltmeier M. Deep Gaussian mixture model for unsupervised image segmentation. arXiv.org. Published 2024. https://arxiv.org/abs/2404.12252
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