  An automated and reliable processing of bubbly flow images is highly needed
to analyse large data sets of comprehensive experimental series. A particular
difficulty arises due to overlapping bubble projections in recorded images,
which highly complicates the identification of individual bubbles. Recent
approaches focus on the use of deep learning algorithms for this task and have
already proven the high potential of such techniques. The main difficulties are
the capability to handle different image conditions, higher gas volume
fractions and a proper reconstruction of the hidden segment of a partly
occluded bubble. In the present work, we try to tackle these points by testing
three different methods based on Convolutional Neural Networks (CNNs) for the
two former and two individual approaches that can be used subsequently to
address the latter. To validate our methodology, we created test data sets with
synthetic images that further demonstrate the capabilities as well as
limitations of our combined approach. The generated data, code and trained
models are made accessible to facilitate the use as well as further
developments in the research field of bubble recognition in experimental
images.
