用深度学习提升背景光栅法的图像去噪与条纹解调精度
Deep learning approach for flow visualization in background-oriented schlieren
- 基于深度学习的子空间方法，增强条纹图解调能力
- 在强噪声和不均匀畸变下仍保持高精度解调
- 适用于真实液体扩散过程的实验验证
基于衍射光学元件的背景光栅法（BOS）是一种广泛用于流场定量可视化的方法。该技术通过将测试介质的空间密度变化编码为光学条纹图案来实现可视化，因此其精度直接受条纹图解调质量的影响。本文提出一种鲁棒的深度学习辅助子空间方法，能够在严重噪声和不均匀条纹畸变条件下仍实现可靠的条纹图解调。通过严格的数值模拟验证了该方法对条纹伪影的处理能力；此外，还利用真实液态扩散过程采集的BOS图像进行了实验验证，证明了该方法在实际应用中的有效性。
原文摘要 · Abstract (English)
Diffractive optical element based background oriented schlieren (BOS) is a popular technique for quantitative flow visualization. This technique relies on encoding spatial density variations of the test medium in the form of an optical fringe pattern; and hence, its accuracy is directly influenced by the quality of fringe pattern demodulation. We introduce a robust deep learning assisted subspace method which enables reliable fringe pattern demodulation even in the presence of severe noise and uneven fringe distortions in recorded BOS fringe patterns. The method's effectiveness to handle fringe pattern artifacts is demonstrated via rigorous numerical simulations. Furthermore, the method's practical applicability is experimentally validated using real-world BOS images obtained from a liquid diffusion process.
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