Abstract Optical photography and image analysis technology provide a non-intrusive and efficient research tool for the experimental research of multiphase bubbly flow. This paper presented a multi-frame image processing algorithm for measuring size and velocity of bubbles more accurately with bubble shadow images, especially in bubbly plume with serious overlapping in the bubble image. The raw images are the accurate recording of bubble shadow by high frequency CCD camera. In the shadow image, some of bubbles are isolated and others may overlap resulting in a complex geometric shape. It is easy to obtain the shape and centroid of isolated bubbles. But for the overlapping bubbles, additional effort should be made to detect each one from a cluster. And some of them are highly and completely overlapped, and may be lost during tracking. The main conception of the algorithm is based on the continuity of bubble movement to retrieve those bubbles. In one frame, a bubble which is highly or completely overlapped by others may appear alone in another frame. With the information of previous frames, the centroid and size of highly overlapping bubbles are predicted and then corrected according internal cores and edge information of bubble shadow. The algorithm is tested by artificial bubble images and further applied to the experiment of bubbly plume. It is proved that the algorithm performs well in recognition of bubbles and especially in determination of the size and velocity of bubbles in a cluster. For the artificial bubble images, the proposed algorithm successfully captures all bubbles including an inner bubble which cannot be detected in a single frame. For the experiment of bubbly plume, which is conducted in a tank with a 1 cm diameter nozzle at bottom and with the superficial gas velocity ranging from 31.8 to 53.1 mm/s, the effectiveness of the proposed method also reaches 95% by random sampling. For the case that more than half of bubbles are overlapped, the new algorithm can improve the recognition rate from 4% to 6% comparing with the primary algorithm without predictor-corrector method.

中文翻译：

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一种使用预测校正法测量多相气泡流的图像处理算法

摘要 光学摄影和图像分析技术为多相气泡流的实验研究提供了一种非侵入式、高效的研究工具。本文提出了一种多帧图像处理算法，可以利用气泡阴影图像更准确地测量气泡的大小和速度，尤其是在气泡图像中重叠严重的气泡羽流中。原始图像是高频CCD相机对气泡影的准确记录。在阴影图像中，一些气泡是孤立的，而其他气泡可能会重叠，从而导致复杂的几何形状。很容易获得孤立气泡的形状和质心。但是对于重叠的气泡，应该做出额外的努力来检测集群中的每个气泡。并且其中一些高度且完全重叠，并且可能在跟踪过程中丢失。该算法的主要概念是基于气泡运动的连续性来检索这些气泡。在一帧中，与其他人高度或完全重叠的气泡可能会单独出现在另一帧中。利用前一帧的信息，预测高度重叠气泡的质心和大小，然后根据气泡阴影的内部核心和边缘信息进行校正。该算法通过人工气泡图像进行测试，并进一步应用于气泡羽流实验。证明该算法在气泡识别方面表现良好，尤其是在确定簇内气泡的大小和速度方面表现良好。对于人工气泡图像，所提出的算法成功地捕获了所有气泡，包括在单帧中无法检测到的内部气泡。对于气泡羽流实验，该实验在底部直径为 1 cm 喷嘴的罐中进行，表观气速范围为 31.8 至 53.1 mm/s，该方法随机采样的有效性也达到了 95%。对于超过一半的气泡重叠的情况，新算法与没有预测校正方法的原算法相比，可以将识别率从4%提高到6%。