Improvement of Spatial Resolution of W-BOS Analysis Images in Supersonic Flow Using Super-Resolution Technique
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Abstract
The Wavelet-based Background Oriented Schlieren (W-BOS) method quantitatively evaluates the density gradient. This study achieves high spatial and temporal resolution in the density gradient images by integrating a super-resolution technique and the W-BOS method. This experiment uses a jet and a jet-induced shock wave emitted from an open small-volume shock tube as visualization targets. We investigated the effect of super-resolution model types and restoration magnification ratios. Three established super-resolution models are used to restore. The magnifications of 2, 4 and 8 are evaluated. At magnifications 2 and 4, the processed images were close to the original, with the shock wave and the jet well captured in all super-resolution models. In the restoration of magnification 4 using the EDSR model, a measurement error was about 15% smaller than a Bicubic interpolation. These results suggest that the W-BOS method is capable of super-resolution restoration of high-resolution images with magnifications up to 4.
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Meier G. Computerized background-oriented schlieren. Experiments in Fluids. 2002;33:181-187.
Dalziel S, Hughes G, Sutherland B. Whole-field density measurements by ‘synthetic schlieren’. Experiments in Fluids. 2000;28(4):322-335.
Akatsuka J, Nagai S, Honami S. Improved Flow Visualization Methods Based on the Background Oriented Schlieren Technique. Transactions of the Japan Society of Mechanical Engineers. 2011;77(784):2391-2400.
Miyaoku K, Fukuoka H, Nakamura S, Yao M, Hiro K. Improvement of Background Oriented Schlieren Method Focused on Amplitude of Wavelet Transform. IOP Conf. Series: Materials Science and Engineering. 2019;886(012037):1-8.
Rajshekhar G, Ambrosini D. Multi-scale approach for analyzing convective heat transfer flow in background-oriented Schlieren technique. Optics and Lasers in Engineering. 2018;110:415-419.
Schmidt B, Woike M. Wavelet-Based Optical Flow Analysis for Background-Oriented Schlieren Image Processing. AIAA Journal. 2021;59(8):1-20.
Ramaiah J, Rubeis T, Gannavarpu R, Ambrosini D. Quantitative flow visualization by hidden grid background oriented schlieren. Optics and Lasers in Engineering. 2023;160(107307):1-7.
Kong C, Chang J, Wang Z, Li Y, Bao W. Data-driven super-resolution reconstruction of supersonic flow field by convolutional neural networks. AIP Advances. 2021;11(6):1-14.
Ekwonu M, Zhang S, Chen B. Super-resolution reconstruction of schlieren images of supersonic free jets based on machine learning with bubble shadowgraphy data. Journal of Visualization. 2023;26:1085-1099.
Wang Z, Li X, Liu L, Wu X, Hao P, Zhang X, He F. Deep-learning-based super-resolution reconstruction of high-speed imaging in fluids. Physics of Fluids. 2022;34(3):1-22.
Ota K, Ukai T, Wakai T. Spatial resolution improvement by a super-resolution technique depending on training process in the background-orientated schlieren analyses. Physics of Fluids. 2023;35(12):1-11.
