A hybrid image processing method for measuring 3D bubble distribution using digital inline holography

Abstract

The paper presents a hybrid bubble hologram processing approach for measuring the size and 3D distribution of bubbles over a wide range of size and shape. The proposed method consists of five major steps, including image enhancement, digital reconstruction, small bubble segmentation, large bubble/cluster segmentation, and post-processing. Two different segmentation approaches are proposed to extract the size and the location of bubbles in different size ranges from the 3D reconstructed optical field. Specifically, a small bubble is segmented based on the presence of the prominent intensity minimum in its longitudinal intensity profile, and its depth is determined by the location of the minimum. In contrast, a large bubble/cluster is segmented using a modified watershed segmentation algorithm and its depth is measured through a wavelet-based focus metric. Our processing approach also determines the inclination angle of a large bubble with respect to the hologram recording plane based on the depth variation along its edge on the plane. The accuracy of our processing approach on the measurements of object size and 3D distributions are assessed through synthetic bubble holograms and oil droplet holograms from an experiment separately. In addition, we evaluate the ability of this algorithm to estimate the bubble inclination with respect to the hologram recording plane through measuring a 3D-printed physical target of pillars with different inclination angles. The holographic measurement technique is further implemented to capture the fluctuation of instantaneous gas leakage rate from a ventilated supercavity generated in a water tunnel experiment. Overall, our paper introduces an inexpensive and compact solution for high resolution characterization of bubbles and other particles in multiphase flows from a broad range of applications.