BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images

TL;DR

BubGAN is a novel GAN architecture that generates realistic, labeled bubbly flow images with controllable bubble features, aiding in training and benchmarking image processing algorithms for bubbly flows.

Contribution

The paper introduces BubGAN, a GAN that produces labeled, controllable bubbly flow images, improving over conventional models by providing detailed bubble information without manual labeling.

Findings

Generated images are more realistic than conventional models.

The dataset contains over a million labeled bubble images.

The tool facilitates training and benchmarking of image processing algorithms.

Abstract

Bubble segmentation and size detection algorithms have been developed in recent years for their high efficiency and accuracy in measuring bubbly two-phase flows. In this work, we proposed an architecture called bubble generative adversarial networks (BubGAN) for the generation of realistic synthetic images which could be further used as training or benchmarking data for the development of advanced image processing algorithms. The BubGAN is trained initially on a labeled bubble dataset consisting of ten thousand images. By learning the distribution of these bubbles, the BubGAN can generate more realistic bubbles compared to the conventional models used in the literature. The trained BubGAN is conditioned on bubble feature parameters and has full control of bubble properties in terms of aspect ratio, rotation angle, circularity and edge ratio. A million bubble dataset is pre-generated using the trained BubGAN. One can then assemble realistic bubbly flow images using this dataset and associated image processing tool. These images contain detailed bubble information, therefore do not require additional manual labeling. This is more useful compared with the conventional GAN which generates images without labeling information. The tool could be used to provide benchmarking and training data for existing image processing algorithms and to guide the future development of bubble detecting algorithms.