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Microscopic Image Augmentation Using an Enhanced WGAN

Themain obstacle to image augmentation withGenerative Adversarial Networks (GANs) is the need for a large amount of training data, but this is difficult for small datasets like Environmental Microorganisms (EMs). To this end, we propose an Enhanced Wasserstein Generative Adversarial Networks (E-WGAN) that combines geometric transformation methods and Wasserstein Generative Adversarial Networks (WGAN) for EM image augmentation. First of all, the color of an EM image has an insignificant impact on its class label, based on this fact, we perform color space augmentation to the original EM images. Secondly, we train WGAN with augmented EM images to generate utterly new EM images. Finally, we rotate the generated samples in various directions to obtain a more natural performance. In the experiment, we use VGG16 network to evaluate the proposed E-WGAN on 21 classes of EMs. The average precisions (APs) with VGG16 increase between 0.46% and 73.16% on 19 EM classes, showing the effectiveness and potential of the proposed E-WGAN.