Aberration correction for flow velocity measurements using deep convolutional neural networks

Abstract

Optical imaging-based flow measurement techniques, like particle image velocimetry, are vulnerable to optical distortions caused by inhomogeneous refractive index or fluctuating phase boundaries. These distortions can lead to blurred particle images and uncertain tracer particle position assignment, resulting in a degradation of velocity measurement accuracy. In order to improve the measurement accuracy, adaptive optics system can be applied to correct distortions. For imaging metrology in fluid mechanics, the optical distortions have features of large frequency range, high spatial frequency and large dynamic range. Actuator-based approaches are limited by its performances. In our work, a novel intelligent adaptive optic system was applied to flow measurement, a learning-based aberration correction method without wavefront corrector was demonstrated, which was used to correct distortions in imaging-based flow measurement. A particle image velocimetry setup which can measure wavefront aberration was built to generate training and test dataset for deep neural network, and also the distortion caused PIV image degradation model. The correction performance of the trained neural network was quantitatively evaluated by corrected PIV image quality and flow measurement result.