Whether the lung is infected by COVID-19 can be effectively detected from lung computed tomography (CT) images by the computer-aided diagnosis system whose training is based on deep learning. However, the main problem is the lack of high-quality labeled CT images available for training. This study proposes a method of augmenting lung CT images with the generative adversarial network (GAN). Specifically, labels of different infected areas are generated, and Poisson fusion is performed to enhance the diversity of the generated images. Then, image transformation and generation are implemented by training the GAN model to fulfill the purpose of augmenting the CT image. Segmentation experiments based on the augmented data are also carried out to verify the effectiveness of the data generated. The results of the image generation and segmentation experiments both show that the proposed image generation method achieves favorable effects.